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Moustafa, Ahmed, and Johan Danmo. "Wearable Sensors in Prosthetic Socket." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263928.
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There is a great interest among researchers and clinicians to monitor pressure distributions within prosthetic sockets. Such an application may allow the assessment of the user's comfort and identify problematic areas inside the socket. The sensor that is used within such an application is the Force Sensitive Resistor (FSR). In our research, two types of those FSR's; QTSS (Quantum Technology Supersensor) prototype and Interlink FSR, were tested under different static and cyclic loading conditions to compare sensor properties namely hysteresis, drift and repeatability. The sensors were placed on two types of surfaces; silicone shore 20 A and plexiglass, in order to study the effect of hardness on the performance of the sensors. QTSS performs its worst with 109.5 percent static drift under silicone surface with 185 kPa. Its performance significantly improves under a higher load for plexiglass, with 5.4 percent drift at 348 kPa. Interlink, on the other hand, performs relatively well in both cases, with a highest recorded percentage static drift of 3.2 percent with a silicone surface and a pressure of 185 kPa. Moreover, it was shown that not allowing the sensor to rest between load application had a positive effect on the QTSS, as it recorded a drift of 3.1 percent on plexiglass at a pressure of 348 kPa. QTSS recorded worse performance for hysteresis as well as repeatability than the Interlink FSR. Finally, a sensor matrix was fabricated with the QTSS in order to create a pressure-sensing map that was placed underneath the shoes as one participant walked. The results looked promising as clear identification of at least 3 phases within the gait cycle. It needs to be stated that the QTSS sensor used for this project is an early prototype and many modifications have been made to this sensor since the start of this thesis. Therefore, new study should be performed on this sensor before drawing any firm conclusions on its performance.Det finns ett stort intresse bland forskade och läkare att kunna övervaka tryckfördelningen inuti en benprotes. En sådan lösning kan möjliggöra bedömningen om användarens komfort och identifiera problematiska områden i benprotesen som bör åtgärdas. En sensor som kan användas i en sådan lösning kallas Force Sensitive Resistor (FSR). Detta mastersarbete har jämfört och testat två typer av FSR. Den första sensorn är en prototyp och kommer från företaget, Quantum Technology Supersensor (QTSS) och den andra sensorn säljs kommersiellt och kommer från företaget, Interlink. Sensorerna utsattes för statiska och dynamiska trycktester för att jämföra egenskaper som hysteres, drift och repeterbarhet. Sensorerna placerades även på två typer av underlag vid dessa tester. Det första underlaget var silikon med en hårdhet på 20 A och det andra var plexiglas. Detta gjordes för att dokumentera effekten av materialets hårdhet som omgav sensorerna vid testerna. QTSS sensorn nådde 109,5 % i statisk drift på silikon med ett tryck på 185 kPa. Procentantalet minskar betydligt vid högre vikt och med plexiglas som material, vilket resulterade i 5,4 % statisk drift med ett tryck på 348 kPa. Sensorn från Interlink presterade dock relativt bra vid båda testerna. Den högsta uppmätta statiska driften var 3,2 % och inträffade då sensorn placerades på silikon med ett tryck på 185 kPa. Vidare visade det sig att sensorn från QTSS presterade bättre när den inte tilläts vila mellan testerna. Med ett tryck på 348 kPa på plexiglas hade sensorn från QTSS en statisk drift på 3,1 %. Sensorn från QTSS presterade sämre vid hysteres- och repeterbarhettesterna än sensorn från Interlink. Vidare tillverkades en sensormatris, som sensorn från QTSS var integrerad i, för att kunna studera tryckfördelningen i en benprotes. I brist på tid och utrustning kunde tester på en artificiell benprotes inte utföras. Sensorn placerades därför på undersidan av en sko för att avgöra ifall det finns ett mönster i tryckfördelningen när en testperson går med denna sko. Resultatet var lovande, då det var möjligt att identifiera minst 3 faser i en gångcykel. En viktig sidoflik är att sensorn från QTSS som användes i detta masterarsbete är en tidig prototyp och att många modifikationer har gjorts på denna typ av sensor sedan starten av denna studie. Det är därför viktigt att en ny studie bör utföras med en senare version av denna sensor innan slutsatser kan dras om sensorns prestanda.
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Clarkson, Brian Patrick 1975. "Life patterns : structure from wearable sensors." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8030.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, February 2003.Includes bibliographical references (leaves 123-129).
In this thesis I develop and evaluate computational methods for extracting life's patterns from wearable sensor data. Life patterns are the reoccurring events in daily behavior, such as those induced by the regular cycle of night and day, weekdays and weekends, work and play, eating and sleeping. My hypothesis is that since a "raw, low-level" wearable sensor stream is intimately connected to the individual's life, it provides the means to directly match similar events, statistically model habitual behavior and highlight hidden structures in a corpus of recorded memories. I approach the problem of computationally modeling daily human experience as a task of statistical data mining similar to the earlier efforts of speech researchers searching for the building block that were believed to make up speech. First we find the atomic immutable events that mark the succession of our daily activities. These are like the "phonemes" of our lives, but don't necessarily take on their finite and discrete nature. Since our activities and behaviors operate at multiple time-scales from seconds to weeks, we look at how these events combine into sequences, and then sequences of sequences, and so on. These are the words, sentences and grammars of an individual's daily experience. I have collected 100 days of wearable sensor data from an individual's life. I show through quantitative experiments that clustering, classification, and prediction is feasible on a data set of this nature. I give methods and results for determining the similarity between memories recorded at different moments in time, which allow me to associate almost every moment of an individual's life to another similar moment. I present models that accurately and automatically classify the sensor data into location and activity.
(cont.) Finally, I show how to use the redundancies in an individual's life to predict his actions from his past behavior.
by Brian Patrick Clarkson.
Ph.D.
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Ojetola, O. "Detection of human falls using wearable sensors." Thesis, Coventry University, 2013. http://curve.coventry.ac.uk/open/items/93d006a7-540d-4ceb-8e19-df03e2f6c67f/1.
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Wearable sensor systems composed of small and light sensing nodes have the potential to revolutionise healthcare. While uptake has increased over time in a variety of application areas, it has been slowed by problems such as lack of infrastructure and the functional capabilities of the systems themselves. An important application of wearable sensors is the detection of falls, particularly for elderly or otherwise vulnerable people. However, existing solutions do not provide the detection accuracy required for the technology to gain the trust of medical professionals. This thesis aims to improve the state of the art in automated human fall detection algorithms through the use of a machine learning based algorithm combined with novel data annotation and feature extraction methods. Most wearable fall detection algorithms are based on thresholds set by observational analysis for various fall types. However, such algorithms do not generalise well for unseen datasets. This has thus led to many fall detection systems with claims of high performance but with high rates of False Positive and False Negative when evaluated on unseen datasets. A more appropriate approach, as proposed in this thesis, is a machine learning based algorithm for fall detection. The work in this thesis uses a C4.5 Decision Tree algorithm and computes input features based on three fall stages: pre-impact, impact and post-impact. By computing features based on these three fall stages, the fall detection algorithm can learn patterns unique to falls. In total, thirteen features were selected across the three fall stages out of an original set of twenty-eight features. Further to the identification of fall stages and selection of appropriate features, an annotation technique named micro-annotation is proposed that resolves annotation-related ambiguities in the evaluation of fall detection algorithms. Further analysis on factors that can impact the performance of a machine learning based algorithm were investigated. The analysis defines a design space which serves as a guideline for a machine learning based fall detection algorithm. The factors investigated include sampling frequency, the number of subjects used for training, and sensor location. The optimal values were found to be10Hz, 10 training subjects, and a single sensor mounted on the chest. Protocols for falls and Activities of Daily Living (ADL) were designed such that the developed algorithms are able to cope under a variety of real world activities and events. A total of 50 subjects were recruited to participate in the data gathering exercise. Four common types of falls in the sagittal and coronal planes were simulated by the volunteers; and falls in the sagittal plane were additionally induced by applying a lateral force to blindfolded volunteers. The algorithm was evaluated based on leave one subject out cross validation in order to determine its ability to generalise to unseen subjects. The current state of the art in the literature shows fall detectors with an F-measure below 90%. The commercial Tynetec fall detector provided an F-measure of only 50% when evaluated here. Overall, the fall detection algorithm using the proposed micro-annotation technique and fall stage features provides an F-measure of 93% at 10Hz, exceeding the performance provided by the current state of the art.
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Besrour, Marouen. "Wearable electronic sensors for vital sign monitoring." Master's thesis, Université Laval, 2018. http://hdl.handle.net/20.500.11794/29543.
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On propose dans ce mémoire un nouveau type de capteur pour la mesure des fonctions respiratoires et cardiaques à des fins médicales. Le système offre la possibilité de mesurer le rythme respiratoire et la profondeur de respiration et de transmettre les données vers une station locale pour une analyse plus poussé et un diagnostic. Le capteur proposé est basé sur une approche électromagnétique où on utilise deux antennes posées sur la cage thoracique du patient. Lorsque le patient inspire et expire l’air avec ses poumons, le diamètre de la cage thoracique de ce dernier va augmenter et par conséquent la distance entre les deux antennes aussi. Le système mesure l’écart relatif entre les deux pour extraire le rythme respiratoire. Le point clé du capteur est d’encoder le signal de respiration sous forme de différence de phase entre l’onde émise et l’onde reçue conférant au système une bonne immunité contre les bruits des signaux externes. Le design a été implémenté sur un PCB (46mm x 46mm) pour fournir une preuve de concept de la méthode proposée. Les tests ont été conduits sur trois sujets de deux sexes et d’âges distincts. Les données mesurées démontrent que le système fonctionne sur différentes morphologies physiques. Finalement, le capteur a été capable de recueillir avec grande précision le rythme respiratoire et même la fréquence cardiaque.We propose in this project a wearable electronic Patch Radar sensor that can monitor respiration rate and respiration depth continuously in real-time and transmit data to a base station for analysis. The device relies on a two-antenna configuration. Both antennas are bent to the patient chest, and when the patient breathes, the mechanical movement of the chest wall changes the distance between them. The system measures the relative distance between the antennas to extract the respiration pattern. The key feature of the sensor is that it transduces respiration movements to phase shifts in RF wave signals which make it very robust against external interferences. The design was implemented on a PCB (46mm x 46mm) to demonstrate a proof of concept for the proposed device. The system was able to acquire respiration signals and even cardiac frequency. Experimental results are presented for three different subjects, an adult male and female and a child. The data gathered gives enough sensitivity and accuracy to state that the device can work with different physical morphologies.
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Zellers, Brian Andrew. "3D Printed Wearable Electronic Sensors with Microfluidics." Youngstown State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1575874880525156.
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Bharti, Pratool. "Context-based Human Activity Recognition Using Multimodal Wearable Sensors." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/7000.
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In the past decade, Human Activity Recognition (HAR) has been an important part of the regular day to day life of many people. Activity recognition has wide applications in the field of health care, remote monitoring of elders, sports, biometric authentication, e-commerce and more. Each HAR application needs a unique approach to provide solutions driven by the context of the problem. In this dissertation, we are primarily discussing two application of HAR in different contexts. First, we design a novel approach for in-home, fine-grained activity recognition using multimodal wearable sensors on multiple body positions, along with very small Bluetooth beacons deployed in the environment. State-of-the-art in-home activity recognition schemes with wearable devices are mostly capable of detecting coarse-grained activities (sitting, standing, walking, or lying down), but cannot distinguish complex activities (sitting on the floor versus on the sofa or bed). Such schemes are not effective for emerging critical healthcare applications – for example, in remote monitoring of patients with Alzheimer's disease, Bulimia, or Anorexia – because they require a more comprehensive, contextual, and fine-grained recognition of complex daily user activities. Second, we introduced Watch-Dog – a self-harm activity recognition engine, which attempts to infer self-harming activities from sensing accelerometer data using wearable sensors worn on a subject's wrist. In the United States, there are more than 35,000 reported suicides with approximately 1,800 of them being psychiatric inpatients every year. Staff perform intermittent or continuous observations in order to prevent such tragedies, but a study of 98 articles over time showed that 20% to 62% of suicides happened while inpatients were on an observation schedule. Reducing the instances of suicides of inpatients is a problem of critical importance to both patients and healthcare providers. Watch-dog uses supervised learning algorithm to model the system which can discriminate the harmful activities from non-harmful activities. The system is not only very accurate but also energy efficient. Apart from these two HAR systems, we also demonstrated the difference in activity pattern between elder and younger age group. For this experiment, we used 5 activities of daily living (ADL). Based on our findings we recommend that a context aware age-specific HAR model would be a better solution than all age-mixed models. Additionally, we find that personalized models for each individual elder person perform better classification than mixed models.
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Simoes, Mario Alves. "Feasibility of Wearable Sensors to Determine Gait Parameters." Scholar Commons, 2011. http://scholarcommons.usf.edu/etd/3346.
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A wearable system that can be used in different settings to collect gait parameters on subjects with a mild traumatic brain injury (mTBI) would allow clinicians to collect needed data of subjects outside of the laboratory setting. Mild traumatic brain injuries stem from a number of causes such as illnesses, strokes, accidents or battlefield traumas. These injuries can cause issues with everyday tasks, such as gait, and are linked with vestibular dysfunction [1]. Different wearable sensor systems were analyzed prior to starting this study along with relevant gait parameters associated with mild traumatic brain injury. To monitor gait parameters relevant to mild traumatic brain injury (cadence, torso rate of rotation, head rate of rotation and stride length) a wearable sensor system was selected (APDM Opal Movement Monitor [13]) and compared against the gold standard optical tracking system (Vicon) [2]. A group of ten, 20-27 year old, healthy subjects were used to validate the APDM Movement Monitor system using the Pearson's R correlation value [35]. Subjects were asked to wear the APDM movement monitors in conjunction with the reflective markers of the Vicon system while performing three sessions of gait trials: a normal gait speed, a fast gait speed and a slow gait speed. Using the Pearson's R correlation values, cadence, torso rate of rotation, and head rate of rotation were found to be highly correlated between both systems. The Pearson's R correlations for cadence, torso rate of rotation, head rate of rotation and stride length were 0.967, 0.907, 0.942, and 0.861, respectively. These correlation values suggest the gait parameters relevant to mild traumatic brain injury are highly correlated between both the APDM Movement Monitor system and the Vicon system, and APDM's wearable sensor system was lightweight, portable and less costly than the Vicon system.
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Reyes, Sabrina Ensign. "Evaluating human-EVA suit injury using wearable sensors." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105623.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 81-82).
All the current flown spacesuits are gas pressurized and require astronauts to exert a substantial amount of energy in order to move the suit into a desired position. The pressurization of the suit therefore limits human mobility, causes discomfort, and leads to a variety of contact and strain injuries. While suit-related injuries have been observed for many years and some basic countermeasures have been implemented, there is still a lack of understanding of how humans move within the spacesuit. The rise of wearable technologies is changing the paradigm of biomechanics and allowing a continuous monitoring of motion performance in fields like athletics or medical rehabilitation. Similarly, pressure sensors allow a sensing capability to better locate the areas and magnitudes of contact between the human and their interface and reduce the risk of injuries. Coupled together these sensors allow a better understanding of the complex interactions between the astronaut and his suit, enhance astronauts performance through a real time monitoring and reducing the risk of injury. The first set of objectives of this research are: to gain a greater understanding of this human-spacesuit interaction and potential for injury by analyzing the suit-induced pressures against the body, to determine the validity of the particular sensors used with suggested alternatives, and to extend the wearable technology application to other relatable fields such as soldier armor and protective gear. An experiment was conducted in conjunction with David Clark Incorporated Company on the Launch Entry Development spacesuit analyzing the human-spacesuit system behavior for isolated and functional upper body movement tasks: elbow flexion/extension, shoulder flexion/extension, shoulder abduction/adduction and cross body reach, which is a complex succession of critical motions for astronaut and pilot task. The contact pressure between the person and the spacesuit was measured by three low-pressure sensors (the Polipo) over the arm, and one high-pressure sensor located on the shoulder (Novel). The same sensors were used in a separate experiment conducted in conjunction with Protect the Force Company on several different United States Marine Corps (USMC) protective gear configurations, which analyzed the human-gear interactions for: shoulder flexion/extension, horizontal shoulder abduction/adduction, vertical shoulder abduction/adduction, and the cross body reach. Findings suggest that as suit pressurization increases, contact pressure across the top of the shoulder increases for all motion types. While it proved to be a perfectly acceptable method for gathering shoulder data, improvements can be made on the particular sensors used and the type of data collected and analyzed. In the future, human-suit interface data can be utilized to influence future gas-pressurized spacesuit design. Additionally, this thesis briefly explores the incompatibilities between Russian and U.S. EVA capabilities in order to make a case for equipment standardization.
by Sabrina Reyes.
S.M.
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Ali, Syed Muhammad Raza. "Behaviour profiling using wearable sensors for pervasive healthcare." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/10929.
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In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined.
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Dello, Preite Davide. "M-Health: analisi e sviluppo dei wearable sensors." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3092/.
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Junker, Holger. "Human activity recognition and gesture spotting with body-worn sensors /." Konstanz : Hartung-Gorre, 2005. http://www.loc.gov/catdir/toc/fy0608/2006356170.html.
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Palanisamy, Asha. "High Energy Density Battery for Wearable Electronics and Sensors." University of Dayton / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1480511507315736.
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AWAID, MOSTAFA. "Human upper limb movement assessment based on wearable sensors." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2015. http://hdl.handle.net/2108/211154.
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The advances in medical technologies have continued to improve diagnostic and measurement devices, eventually leading to better healthcare. Glove-based systems represent one of the most important efforts aimed at acquiring hand movement data. On the other hand, inertial motion sensor is the best device for biomechanics studies because it is transportable, small, low cost, ease to be set up and less burdensome to the subject. However, processing and accuracy of the data obtained are essential issues for research purposes. The work in this dissertation are divided into three studies as follow:
The first, is to analyze hand function after hand surgery as it represents the first step the clinician has to address before defining the rehabilitation procedure and to follow the rehabilitation process for any patient. In this research, the Range of Motion (ROM) for all fingers and the ability of participants to repeat two ADL (Activities of Daily Living)-based tasks were investigated. For two patient subjects, the results were compared to that of healthy subjects. The major goals is to furnish the clinicians a tool capable of measuring objectively human hand movements and quantify the recovery of motor function during the rehabilitation.
The second study, is to validate the inertial-based system as a wearable sensor for upper limb motion analysis with optical tracking system used for co-registration as a gold standard system. The estimated angles between elbow and wrist joints of two systems were compared to address the challenges. Furtherly the inertial sensors allow clinicians to record human movements performed in normal activity daily life, so that they can remotely study the results. It is capable of tracking human upper limb motions, reconstructing the human model, monitoring human health status, positioning and recording human itinerary in ADL environment. The analyzed results will be used to know the accuracy and correlation between two systems.
The last study, is to validate the inertial sensor device with minimal human intervention. In addition, design a tri-axial rotational set up (Gimbal) to be used as a reference. The comparison between the actual angular velocity of the inertial sensor and different constant angular speeds of Gimbal was done to calculate the coincident and error with rotating speeds.
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MUSU, CLAUDIA. "Wearable sensors networks for safety applications in industrial scenarios." Doctoral thesis, Università degli Studi di Cagliari, 2015. http://hdl.handle.net/11584/266605.
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Industrial contexts, and in particular the port areas, are very complex systems to be monitored
and controlled due to the combined presence of vehicles and people. The port areas are the gateway
between navigation and terrestrial transportation and are of great importance in transport logistics.
Unfortunately, the management of port areas is quite complex because the safety of the workers
must be always assured. Therefore, in such a context, a centralized control system for the
monitoring and the prevention of risks is of particular importance. In this thesis, a real-time control
system for the monitoring of people and vehicles in industrial areas is proposed. The proposed
system is based on the Internet of Things paradigm, i.e. a network of “things” (such as sensors, tag
RFID, actuators etc.) which can communicate and interact with each other within a shared IP
addressing range, in order to share data and contribute to the management and development of
advanced applications. Specifically, the thesis is focused on the design of a wearable sensors
network based on RFID technology, and specifically on WISP sensors, for assuring the safety of the
workers. In this network, wearable devices that can be inserted directly on the textile have been
selected. Differently from conventional sensors, wearable sensors ensure a higher level of comfort,
and provide higher electromagnetic performance. Furthermore, textile materials are easily available.
Microstrips are good candidates for these applications because they mainly radiate perpendicularly
to the planar structure, and their ground plane allows a good shielding on the body tissues.
Therefore, I have designed specific antennas for RFID, that unlike the classical microstrip antennas
have the radiating surface composed of several "side by side" conductive "threads of textile". Since
the microwave model does not allow the design of an antenna with these characteristics with a good
approximation, a specific microwave model for coupled lines has been designed. With this model,
the specific antenna for RFID has been designed, with Jeans as substrate. The particular antenna’s
substrate allows direct integration into garments, but since the wearable antennas are placed very
close to the human body, biological issues which may arise on the human body from the use of
these sensors have been analysed. The Specific Absorption Rate (SAR) has been considered and
simulations have been conducted for evaluating the effects on the human body, and especially on
the head, when irradiated with the electromagnetic waves generated by the wearable antenna
realized with different materials. Dosimetric effects have been evaluated in function of the distance
from the body, in order to define a safe distance for placing the antenna on the human body. The
SAR has been evaluated also for full patches with different textile substrates, whose surface is
larger than that of the proposed model of coupled lines. Therefore, if the SAR values evaluated for the full patch are satisfying, the SAR values for the model of coupled lines will surely be
acceptable.
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Guinovart, Pavón Tomàs de Aquino. "Addressing emerging paradigms in chemical analysis: new platforms for wearable and decentralized sensors." Doctoral thesis, Universitat Rovira i Virgili, 2015. http://hdl.handle.net/10803/401830.
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L’objectiu principal d’aquesta tesi és l’exploració de noves fronteres en l’àrea de sensors químics. Primerament, a través del desenvolupament de nous enfocs per construir sensors químics vestibles. Seguidament, a través de la generació de noves maneres de sensar electròlits en mostres reals. La potenciometria i la detecció òptica seran les tècniques utilitzades.
L’ús de la potenciometria cap al 1900, amb els elèctrodes selectius de ions (ESI), ha anat canviant i la revolució va començar amb la miniaturització d’aquests ESI amb l’aparició de la necessitat de sensors per monitoritzar el nostre estat de salut, juntament amb la tendència de desenvolupar sensors vestibles. Aquesta tesi, llavors, ha estat el punt d’inici per al desenvolupament de sensors incorporats en plataformes vestibles, per acabar mesurant electròlits en fluids biològics.
També, aquesta tesi s’ha focalitzat en la millora de les membranes selectives polimèriques, específicament per un analit. Durant la última dècada, hi ha hagut un avenç considerable i recerca de nous receptors per molècules clínicament rellevants present en el nostre cos. Per exemple, la introducció d’un nou receptor artificial (calix[4]pyrrole) per creatinina ha sigut un resultat molt important d’aquesta tesi. La creatinina és la segona molècula més detectada rutinàriament analitzada després de la glucosa, fet el qual probablement pot obrir la porta cap al mercat després d’aquesta tesi.
Finalment, la potenciometria no ha estat l’única tècnica utilitzada en aquesta tesi, sinó que també s’ha utilitzat la tècnica òptica. El repte més important, demostrat en aquesta tesi, és la detecció d’anions difícilment detectables amb la potenciometria, com per exemple l’anió sulfat.El objetivo principal de esta tesis es la exploración de nuevas fronteras en el área de sensores químicos. Primeramente, a través del desarrollo de nuevos enfoques para construir sensores químicos vestibles. Seguidamente, a través de la generación de nuevas maneras de sensar electrolitos en muestras reales. La potenciometría y la detección óptica serán las técnicas utilizadas. El uso de la potenciometría allá en el 1900, con los electrodos selectivos de iones (ESI), ha seguido un proceso de cambio y la revolución empezó con la miniaturización de estos ESI con la aparición de la necesidad de sensores para monitorizar nuestro estado de salud, juntamente con la tendencia de desarrollar sensores vestibles. Esta tesis, entonces, ha sido el punto de inicio para el desarrollo de sensores incorporados en plataformas vestibles, para medir electrolitos en fluidos biológicos. También, esta tesis se ha focalizado en la mejora de las membranas selectivas poliméricas, específicamente para un analito. En la última década, ha habido un avance considerable e investigación de nuevos receptores para moléculas clínicamente relevantes presentes en nuestro cuerpo. Por ejemplo, la introducción de un nuevo receptor artificial (calix[4]pyrrole) para creatinina ha sido un resultado muy importante para esta tesis. La creatinina es la segunda molécula más detectada rutinariamente analizada después de la glucosa, hecho que probablemente puede abrir la puerta al mercado después de la tesis. Finalmente, la potenciometría no ha sido la única técnica utilizada en esta tesis, sino que también se ha utilizado la técnica óptica. El reto más importante, demostrado en esta tesis, es la detección de aniones difícilmente detectables con la potenciometría, como per ejemplo el anión sulfato.
The main objective of this thesis is the exploration of new frontiers in the area of chemical sensors. First, through the development of novel approaches to build wearable chemical sensing devices. Second, through the generation of new sensing approaches to determine electrolytes in liquid samples. Potentiometry as well as optical detection techniques will be used. Since potentiometry was firstly used at the beginning of 1900, with classical ion-selective electrodes (ISEs), this technique have constantly been changing and the revolution of this technique started with the miniaturization of these ISEs in combination with the real need of sensors for monitoring our health status, which has merged in a new trend to develop wearable sensors. This thesis has, then, been the starting point to develop potentiometric sensors embedded in wearable platforms, being finally used for measuring electrolytes in biological fluids. Also, this thesis has been focused on the improvement of the ion-selective polymeric membrane sensitive to one specific electrolyte. In the last decade, there has been a considerable advance and research of new receptors for clinically relevant molecules present in our body. For example, the introduction of a new artificial receptor (calix[4]pyrrole) for creatinine has been a significant milestone in this thesis. Creatinine is the second most important molecule routinely analyzed after glucose, thus likely to open a path to the market beyond this thesis. Finally, potentiometry has not been the only technique used in this thesis but also optical technique. The real challenge, overcome in this thesis, has been to sense anions selectively, especially the ones that currently are hardly measured with potentiometry, such as for example sulphate.
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OTTIKKUTTI, SURANJAN RAM. "Effective Optimization of Deployment for Wearable Sensors in Transfemoral Prosthesis." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289478.
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Transfemoralor above-the-knee amputees face discomfort in their prothesis primarily due to irregular distribution of pressure and shear forces in the Socket-stump interface (SSI). To quantify this discomfort it is necessary to first determine the pressure distribution in the SSI using sensors. However, knowledge of how sensors should be deployed is necessary to support the testing of said pressure on a test-rig or amputee. Previous methods used to determine sensor placement include discretization of the SSI into several regions or the use of a reiterative method based on pressure readings from sensors to determine the optimal placement of sensors. The former fails to identify high regions of pressure as the regions covered by the sensors may not have high pressure whereas the latter is time consuming and may cause further trauma to amputees as it requires repeated experimentation. With the advances in pressure sensor technologies, biomechanical simulations, and Finite elementanalysis(FEA)simulations it is now increasingly possible to determine an accurate estimate of dynamic pressure distribution occurring in the SSI during the gait cycle. The thesis investigates the dynamic pressure distribution in the SSI and determines an effective method of locating the optimal positions for the sensors using two different algorithms. The first is a Genetic Algorithm whereas the second is Pattern Search.Transfemorala eller amputerade över knäet möter obehag i sin protes främst på grund av oregelbunden fördelning av tryck och skjuvkrafter i SSI. För att kvantifiera detta obehag är det nödvändigt att först bestämma tryckfördelningen i SSI med hjälp av sensorer. Men kunskap om hur sensorer ska distribueras är nödvändig för att stödja testningen av nämnda tryck på en testrigg eller amputerad. Tidigare metoder som använts för att bestämma sensorplacering inkluderar diskretisering av SSI i flera regioner eller användning av en upprepad metod baserad på tryckavläsningar från sensorer för att bestämma den optimala placeringen av sensorer. Den förstnämnda misslyckas med att identifiera höga tryckregioner eftersom den områden som täcks av sensorerna kanske inte har högt tryck medan de senare är tidskrävande och kan orsaka ytterligare trauma för amputerade eftersom det kräver upprepade experiment. Med framstegen inom trycksensorteknologier, biomekaniska simuleringar och FEA-simuleringar är det nu alltmer möjligt att bestämma en exakt uppskattning av dynamisk tryckfördelning i SSI under gångcykeln. Avhandlingen undersöker den dynamiska tryckfördelningen i SSI och bestämmer en effektiv metod för att lokalisera de optimala positionerna för sensorerna med hjälp av två olika algoritmer. Den första är en genetisk algoritm medan den andra är mönstresökning
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Parrilla, Pons Marc. "New Electrochemical Sensors for Decentralized Analysis." Doctoral thesis, Universitat Rovira i Virgili, 2016. http://hdl.handle.net/10803/396297.
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Nous sensors electroquímics per a analisis decentralitzats és una tesis que emmarca diferents aspectes del desenvolupament de sensors potenciomètrics, des de la seva fabricació, el diseny adequat, i finalment, la seva aplicabilitat en escenaris reals. En el context actual, l'evolució de la tecnologia, especialment l'aparició a nivell global d'internet, i la disponibilitat d'aquesta a baix cost han permès la creació d'eines que ens permeten connectar el món físic i, en el cas d'aquesta tesis, el món químic a la xarxa. Aquesta connexió aporta un nou grau dins l'escala de valor per a la societat actual. Concretament, aquesta aportació tecnològica va adreçada a superar els nous reptes de l'actualitat, com poden ser la sostenibilitat del sistema sanitari a causa de l'embelliment de la societat, el control medioambiental, així com també mantenir la seguretat per a la societat del benestar del futur. Així doncs, aquesta tesis presenta solucions efectives per al desenvolupament d'eines de captació d'informació que serviràn per nudrir a la societat de major coneixement. Conseqüentment, produint nous negocis al voltant, de la fabricació, processament i creació de valor entorn a aquestes dades. La recerca i desenvolupament de sensors potenciomètrics integrats a la roba per detectar els nivells d'electròlits i sensors senzills de paper per a la determinació de biomolècules, com la glucosa, són alguns dels objectius aconseguits en aquesta tesis. A més a més, sensors integrats en globus permeten l'estudi de les seves propietats mecàniques i electroquímiques, així com també, aporten noves solucions a problemes reals. Totes aquestes aplicacions serveixen de portals de captació d'informació química cap a la integració dins la nova societat de la informació.Nuevos sensores electroquímicos para analisis decentralizados es una tesis que enmarca diferentes aspectos del desarrollo de sensores potenciométricos, desde su fabricación, el diseño adecuado, i finalmente, su aplicabilidad en escenarios reales. En el contexto actual, la evolución de la tecnología, especialmente la aparición a nivel global de internet, y la disponibilidad de esta a bajo coste han permitido la creación de herramientas que nos permiten conectar el mundo físico y, en el caso de esta tesis, el mundo químico a la red. Esta conexión aporta un nuevo grado dentro la escala de valor para la sociedad actual. Concretamente, esta aportación tecnológica va dirigida a superar los nuevos retos de la actualidad, como pueden ser la sostenibilidad del sistema sanitario a causa del envejecimiento de la poblacion, el control medioambiental, así como también mantener la seguridad para la sociedad del bienestar del futuro. Entonces, esta tesis presenta soluciones efectivas para el desarrollo de herramientas de captación de información que servirán para nutrir a la sociedad de un mayor conocimiento. Por consiguiente, produciendo nuevos negocios alrededor, de la fabricación, procesado i creación de valor en los datos obtenidos. La investigación y desarrollo de sensores potenciométricos integrados en la ropa para detectar los niveles de electrolitos y sensores simples en papel para la determinación de biomoléculas, como la glucosa, son algunos de los objetivos conseguidos en esta tesis. Además, sensores integrados en globos permiten el estudio de sus propiedades mecánicas y electroquímicas, así como, aportando nuevas soluciones a problemas reales. Todas estas aplicaciones sirven de portales de captación de información química hacia la integración dentro de la nueva sociedad de la información.
ew Electrochemical Sensors for Decentralized Analysis is a thesis that wisely discuss the developments of potentiometric sensors, from the fabrication step, the use of a suitable design, to the applicability in real scenarios. Nowadays, the evolution of technology, specially the creation of the global internet network, and the low-cost availability of such technology have allowed the development of tools that connect the physical world and, addressed in this thesis, the chemical world into the network. This connection adds a new level in the value chain for the present society. Precisely, this technology approach is focus on circumvent new present challenges of society. For instance, sustainability of the healthcare system caused by the population aging, environmental monitoring, as well as, keep security and safety to the welfare of society of the future. Therefore, this thesis presents successful solutions for the development of tools to gather chemical information. This information will nurture society with high-value knowledge. Accordingly, new business development from, sensing products, data treatment and information management are going to be created. Research and development of potentiometric sensors integrated into garments for electrolyte detection and simple sensors built in paper for biomolecules determination, such as glucose, and liquid monitoring, such as sweat, are some of the accomplished objectives from this thesis. Furthermore, balloon-embedded sensors allow the study of the mechanical and electrochemical properties of the electrodes, as well as, contributing with new solutions to real problems. All the applications developed in this thesis are utilized as gateways for chemical information acquisition towards the integration into the new information society.
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Wong, Charence Cheuk Lun. "Fusion of wearable and visual sensors for human motion analysis." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/28630.
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Human motion analysis is concerned with the study of human activity recognition, human motion tracking, and the analysis of human biomechanics. Human motion analysis has applications within areas of entertainment, sports, and healthcare. For example, activity recognition, which aims to understand and identify different tasks from motion can be applied to create records of staff activity in the operating theatre at a hospital; motion tracking is already employed in some games to provide an improved user interaction experience and can be used to study how medical staff interact in the operating theatre; and human biomechanics, which is the study of the structure and function of the human body, can be used to better understand athlete performance, pathologies in certain patients, and assess the surgical skill of medical staff. As health services strive to improve the quality of patient care and meet the growing demands required to care for expanding populations around the world, solutions that can improve patient care, diagnosis of pathology, and the monitoring and training of medical staff are necessary. Surgical workflow analysis, for example, aims to assess and optimise surgical protocols in the operating theatre by evaluating the tasks that staff perform and measurable outcomes. Human motion analysis methods can be used to quantify the activities and performance of staff for surgical workflow analysis; however, a number of challenges must be overcome before routine motion capture of staff in an operating theatre becomes feasible. Current commercial human motion capture technologies have demonstrated that they are capable of acquiring human movement with sub-centimetre accuracy; however, the complicated setup procedures, size, and embodiment of current systems make them cumbersome and unsuited for routine deployment within an operating theatre. Recent advances in pervasive sensing have resulted in camera systems that can detect and analyse human motion, and small wear- able sensors that can measure a variety of parameters from the human body, such as heart rate, fatigue, balance, and motion. The work in this thesis investigates different methods that enable human motion to be more easily, reliably, and accurately captured through ambient and wearable sensor technologies to address some of the main challenges that have limited the use of motion capture technologies in certain areas of study. Sensor embodiment and accuracy of activity recognition is one of the challenges that affect the adoption of wearable devices for monitoring human activity. Using a single inertial sensor, which captures the movement of the subject, a variety of motion characteristics can be measured. For patients, wearable inertial sensors can be used in long-term activity monitoring to better understand the condition of the patient and potentially identify deviations from normal activity. For medical staff, inertial sensors can be used to capture tasks being performed for automated workflow analysis, which is useful for staff training, optimisation of existing processes, and early indications of complications within clinical procedures. Feature extraction and classification methods are introduced in thesis that demonstrate motion classification accuracies of over 90% for five different classes of walking motion using a single ear-worn sensor. To capture human body posture, current capture systems generally require a large number of sensors or reflective reference markers to be worn on the body, which presents a challenge for many applications, such as monitoring human motion in the operating theatre, as they may restrict natural movements and make setup complex and time consuming. To address this, a method is proposed, which uses a regression method to estimate motion using a subset of fewer wearable inertial sensors. This method is demonstrated using three sensors on the upper body and is shown to achieve mean estimation accuracies as low as 1.6cm, 1.1cm, and 1.4cm for the hand, elbow, and shoulders, respectively, when compared with the gold standard optical motion capture system. Using a subset of three sensors, mean errors for hand position reach 15.5cm. Unlike human motion capture systems that rely on vision and reflective reference point markers, commonly known as marker-based optical motion capture, wearable inertial sensors are prone to inaccuracies resulting from an accumulation of inaccurate measurements, which becomes increasingly prevalent over time. Two methods are introduced in this thesis, which aim to solve this challenge using visual rectification of the assumed state of the subject. Using a ceiling-mounted camera, a human detection and human motion tracking method is introduced to improve the average mean accuracy of tracking to within 5.8cm in a laboratory of 3m x 5m. To improve the accuracy of capturing the position of body parts and posture for human biomechanics, a camera is also utilised to track the body part movements and provide visual rectification of human pose estimates from inertial sensing. For most subjects, deviations of less than 10% from the ground truth are achieved for hand positions, which exhibit the greatest error, and the occurrence of sources of other common visual and inertial estimation errors, such as measurement noise, visual occlusion, and sensor calibration are shown to be reduced.
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BORZI', LUIGI. "Wearable sensors and artificial intelligence for monitoring of Parkinson's disease." Doctoral thesis, Politecnico di Torino, 2023. https://hdl.handle.net/11583/2975707.
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Vatankhah, Varnoosfaderani Mohammad. "Efficient Antennas for Wearable Wireless Sensor Nodes." Thesis, Griffith University, 2016. http://hdl.handle.net/10072/366335.
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Wearable wireless sensors are a part of human life in the future for applications such as healthcare, sports, navigating, security etc. The number of wearable wireless sensors for fitness and healthcare applications will reach 90 million shipments by 2017 base on ABI research report [1]. The IEEE Standard 802.15.6 for local and metropolitan area networks, part 15.6, “Wireless Body Area Networks” was published on February 2012 to specify the short range wireless communication in the vicinity or inside of the human body [2]. This shows the importance of research needed to maximize the quality of wireless communications around the body. The human body is a lossy medium that absorbs the radio frequency (RF) energy and this affects the propagation of electromagnetic waves used in wireless communication. The specific absorption rate (SAR) is defined by the Federal Communications Commission (FCC) to identify criteria for measuring the rate of absorption and amount of interaction between the body and a source of RF energy [3].Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith school of Engineering
Science, Environment, Engineering and Technology
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Ollander, Simon. "Wearable Sensor Data Fusion for Human Stress Estimation." Thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-122348.
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With the purpose of classifying and modelling stress, different sensors, signal features, machine learning methods, and stress experiments have been compared. Two databases have been studied: the MIT driver stress database and a new experimental database, where three stress tasks have been performed for 9 subjects: the Trier Social Stress Test, the Socially Evaluated Cold Pressor Test and the d2 test, of which the latter is not classically used for generating stress. Support vector machine, naive Bayes, k-nearest neighbor and probabilistic neural network classification techniques were compared, with support vector machines achieving the highest performance in general (99.5 ±0.6 %$on the driver database and 91.4 ± 2.4 % on the experimental database). For both databases, relevant features include the mean of the heart rate and the mean of the galvanic skin response, together with the mean of the absolute derivative of the galvanic skin response signal. A new feature is also introduced with great performance in stress classification for the driver database. Continuous models for estimating stress levels have also been developed, based upon the perceived stress levels given by the subjects during the experiments, where support vector regression is more accurate than linear and variational Bayesian regression.I syfte att klassificera och modellera stress har olika sensorer, signalegenskaper, maskininlärningsmetoder och stressexperiment jämförts. Två databaser har studerats: MIT:s förarstressdatabas och en ny databas baserad på egna experiment, där stressuppgifter har genomförts av nio försökspersoner: Trier Social Stress Test, Socially Evaluated Cold Pressor Test och d2-testet, av vilka det sistnämnda inte normalt används för att generera stress. Support vector machine-, naive Bayes-, k-nearest neighbour- och probabilistic neural network-algoritmer har jämförts, av vilka support vector machine har uppnått den högsta prestandan i allmänhet (99.5 ± 0.6 % på förardatabasen, 91.4 ± 2.4 % på experimenten). För båda databaserna har signalegenskaper såsom medelvärdet av hjärtrytmen och hudens ledningsförmåga, tillsammans med medelvärdet av beloppet av hudens ledningsförmågas derivata identifierats som relevanta. En ny signalegenskap har också introducerats, med hög prestanda i stressklassificering på förarstressdatabasen. En kontinuerlig modell har också utvecklats, baserad på den upplevda stressnivån angiven av försökspersonerna under experimenten, där support vector regression har uppnått bättre resultat än linjär regression och variational Bayesian regression.
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Charlton, Peter Harcourt. "Continuous respiratory rate monitoring to detect clinical deteriorations using wearable sensors." Thesis, King's College London (University of London), 2017. https://kclpure.kcl.ac.uk/portal/en/theses/continuous-respiratory-rate-monitoring-to-detect-clinical-deteriorations-using-wearable-sensors(43821666-f390-4cab-9cd2-691c2a5a5fe3).html.
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Lara, Yejas Oscar David. "On the Automatic Recognition of Human Activities using Heterogeneous Wearable Sensors." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4120.
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Delivering accurate and opportune information on people's activities and behaviors has become one of the most important tasks within pervasive computing. Its wide spectrum of potential applications in medical, entertainment, and tactical scenarios, motivates further
research and development of new strategies to improve accuracy, pervasiveness, and eciency.
This dissertation addresses the recognition of human activities (HAR) with wearable sensors in three main regards: In the rst place, physiological signals have been incorporated as a new source of information to improve the recognition accuracy achieved by conventional approaches, which rely on accelerometer signals solely. A new HAR system, Centinela, was born from such concept, employing structural feature extraction along with classier
ensembles, and achieving over 95% of recognition accuracy.
In the second place, real time activity recognition was enabled by Vigilante, a mobile HAR framework under the AndroidTM platform. Providing immediate feedback on the user's activities is especially benecial in healthcare and military applications, which
may require alert triggering or support of decision making. The evaluation demonstrates that Vigilante is energy ecient while maintaining high accuracy (i.e., up to 96.8%) and
low response time. The system features MECLA, a mobile library for the evaluation of classification algorithms, which is also suitable for further machine learning applications.
Finally, the activity recognition accuracy is improved by two new strategies for decision fusion and selection in multiple classier systems: the failure product and the precision-recall dierence. The experimental analysis conrms that the presented methods are benecial, not only for recognizing human activities, but also for many other classication problems.
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Yilmaz, Tuba. "Wearable RF sensors for non-invasive detection of blood-glucose levels." Thesis, Queen Mary, University of London, 2013. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8765.
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Radio frequency (RF) techniques have the potential to provide blood glucose readings through sensing the glucose dependent change in dielectric properties of the biological tissue. Such technique can enable much desired non-invasive and continuous monitoring of blood glucose level. In this work, we present realistic glucose dependence of dielectric properties as well as basic understanding of resonator behaviour while radiating towards the lossy biological tissue. To investigate the potential of RF techniques, two resonators, operating at microwave frequencies when placed radiating towards the biological tissue, are designed and fabricated. The spiral resonator is tested with liquid and semi-solid phantoms containing different amounts of sugar. An analytical formulation to retrieve the dielectric properties of the biological tissues is improved. In order to perform realistic tests, novel tissue mimicking materials for an extremely wide frequency range are proposed. Glucose dependance of the blood mimicking material dielectric properties are further investigated by adding realistic glucose amounts to the blood mimicking material and dielectric spectroscopy is performed. Next, a single pole Cole-Cole model is fitted to the median of the dielectric property measurements. In addition, a patch resonator is simulated with four-layered digital phantom and tested with the four-layered physical tissue mimicking phantom. Finally, a double parameter measurement platform is constructed by combining the patch resonator and a commercial force sensor to perform controlled experiments with humans. Also, the force dependant response of the patch resonator is quantified. Soda tests is performed on five subjects with the platform, all subjects were asked to apply the same level of force. Spiral resonator is also applied to examine the glucose changes of two human subjects during the soda test. The results suggests that, although the glucose-dependance of the dielectric properties is relatively small, the input impedance of a microwave resonator is still sensitive to such small alterations.
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Lapinski, Michael Tomasz. "A platform for high-speed biomechanical analysis using wearable wireless sensors." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/91852.
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Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2013.116
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Includes bibliographical references (pages 263-275).
Humanity's desire to capture and understand motion started in 1878 and has continually evolved. Today, the best-of- breed technology for capturing motion are marker based optical systems that leverage high speed cameras. While these systems are excellent at providing positional information, they suffer from an innate inability to accurately provide fundamental parameters such as velocity and acceleration. The problem is further compounded when the target of capture is high-speed human motion. When applied to biomechanical study, this inaccuracy is magnified when higher order parameters, such as torque and force, are calculated using optical information. This dissertation presents a a first-of-its-kind wearable dual-range inertial sensor platform that allows end-to-end investigation of high level biomechanical parameters. The platform takes a novel approach by providing these parameters more accurately and at a higher fidelity than the current state of the art.The dual-range sensing approach allows accurate capture of both slow-moving motion and rapid movement which pushes the limits of human ability. The platform addresses inherent problems with scaling clinical biomechanical analysis to tens-of-thousands of trials using the sensor platform's data. This end-to-end approach provides mechanisms for rapid player instrumentation, en masse data translation and calculation of clinically relevant joint forces and torques. I present design details for this platform along with kinematic testing and some early biomechanical insight gleamed from system measurements.
by Michael T. Lapinski.
Ph. D.
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Early, Jared W. Early. "Business Opportunity Analysis of Wearable and Wireless Electromyography Sensors in Athletics." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1470652934.
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Adamowicz, Lukas. "Functional Rotation Axis Based Approach for Estimating Hip Joint Angles Using Wearable Inertial Sensors: Comparison to Existing Methods." ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1044.
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Wearable sensors are at the heart of the digital health revolution. Integral to the use of these sensors for monitoring conditions impacting balance and mobility are accurate estimates of joint angles. To this end a simple and novel method of estimating hip joint angles from small wearable magnetic and inertial sensors is proposed and its performance is established relative to optical motion capture in a sample of human subjects. Improving upon previous work, this approach does not require precise sensor placement or specific calibration motions, thereby easing deployment outside of the research laboratory. Specific innovations include the determination of sensor to segment rotations based on functionally determined joint centers, and the development of a novel filtering algorithm for estimating the relative orientation of adjacent body segments. Hip joint angles and range of motion determined from the proposed approach and an existing method are compared to those from an optical motion capture system during walking at a variety of speeds and tasks designed to exercise the hip through its full range of motion. Results show that the proposed approach estimates flexion/extension angle more accurately (RMSE from 7.08 to 7.29 deg) than the existing method (RMSE from 11.64 deg to 14.33 deg), with similar performance for the other anatomical axes. Agreement of each method with optical motion capture is further characterized by considering correlation and regression analyses. Mean ranges of motion for the proposed method are not largely different from those reported by motion capture, and showed similar values to the existing method. Results indicate that this algorithm provides a promising approach for estimating hip joint angles using wearable inertial sensors, and would allow for use outside of constrained research laboratories.
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Araujo, William Reis de. "Desenvolvimento de sensores eletroquímicos e colorimétricos para aplicações em amostras de interesse forense." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/46/46136/tde-18082016-084906/.
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Esta tese apresenta os estudos e esforços visando ao desenvolvimento de sensores químicos para aplicações diversas na área forense. Foram desenvolvidos métodos eletroanalíticos para detecção e quantificação de alguns compostos comumente encontrados na adulteração de amostras de drogas de abuso (procaína, fenacetina, aminopirina, paracetamol, levamisol), além da cocaína e estudos fundamentais sobre o comportamento eletroquímico desses compostos. Empregaram-se também métodos eletroquímicos para quantificação de compostos tóxicos e perigosos como explosivos (ácido pícrico) e melamina por exemplo. Os trabalhos utilizando sensores eletroquímicos contemplam modificações eletroquímicas das superfícies eletródicas, utilização de sensores com polímeros molecularmentes impressos (MIP) e eletrodos descartáveis em papel utilizando diferentes técnicas voltamétricas e amperométricas, eletrodo disco rotatório (EDR) e microbalança de cristal de quartzo. Além da fabricação de dispositivos analíticos descartáveis em papel empregando detecção eletroquímica utilizou-se também a detecção colorimétrica para quantificação de alguns dos principais adulterantes de amostras de apreensão de cocaína, como procaína e fenacetina, bem como análises e discriminações de compostos explosivos (peroxi e nitro compostos) nessas plataformas portáteis e de baixo custo. Os métodos foram sempre desenvolvidos visando característicos como: facilidade, praticidade, baixo custo e portabilidade para análises diretamente no local de medida com mínima infraestrutura laboratorial. Por fim, são apresentados alguns estudos realizados durante estágio de pesquisa no exterior (Universidade da Califórnia - San Diego (UCSD)) na área de Wearable Sensors, em que foram desenvolvidos métodos para análises de micronutrientes no suor (zinco) e um metabólito (ácido úrico) na saliva usando sensores aplicados diretamente no corpo humano.This thesis shows studies and efforts to the development of chemical sensors for different applications in the forensic field. Electroanalytical methods were developed for detection and quantification of some compounds (procaine, phenacetin, aminopyrine, acetaminophen, levamisole) commonly found in the drug of abuse adulteration process and cocaine, as well as, fundamental studies about the electrochemical behavior of these compounds. It was also employed electrochemical methods for quantification of hazardous compounds such as explosives (picric acid) and melamine. Analytical methods with electrochemical sensors included electrochemical modification of electrodic surfaces, molecularly imprinted polymers (MIP), and paper disposable electrochemical devices using different voltammetric and amperometric techniques, rotating disc electrode (RDE) and quartz crystal microbalance. In addition to the fabrication of paper disposable analytical devices with electrochemical detection, it was also used the colorimetric detection to quantify some of the major adulterants in cocaine seizure samples, such as procaine and phenacetin, as well as analysis and discrimination of explosive compounds (peroxy and nitro explosives) in these low cost portable platforms. All proposed methods were always developed aming at theses characteristics: ease, convenience, low cost and portability for analysis directly at the measurement site with minimal laboratory infrastructure. Finally, we presented some studies conducted during research internship abroad (University of California - San Diego (UCSD)) in the area of Wearable Sensors, which have been developed methods for micronutrient analysis in sweat (Zn) and a metabolite (Uric Acid) in saliva using sensors applied directly to the human body
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Martori, Amanda Lynn. "A Wearable Motion Analysis System to Evaluate Gait Deviations." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4724.
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A Wearable Motion Analysis System (WMAS) was developed to evaluate gait, particularly parameters that are indicative of mild traumatic brain injury. The WMAS consisted on six Opal IMUs attached on the sternum, waist, left and right thigh and left and right shank. Algorithms were developed to calculate the knee flexion angle, stride length and cadence parameters during slow, normal and fast gait speeds. The WMAS was validated for repeatability using a robotic arm and accuracy using the Vicon motion capture system, the gold standard for gait analysis. The WMAS calculated the gait parameters to within a clinically acceptable range and is a powerful tool for gait analysis and potential concussion diagnosis outside of a laboratory setting.
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Bergelin, Victor. "Human Activity Recognition and Behavioral Prediction using Wearable Sensors and Deep Learning." Thesis, Linköpings universitet, Matematiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138064.
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When moving into a more connected world together with machines, a mutual understanding will be very important. With the increased availability in wear- able sensors, a better understanding of human needs is suggested. The Dart- mouth Research study at the Psychiatric Research Center has examined the viability of detecting and further on predicting human behaviour and complex tasks. The field of smoking detection was challenged by using the Q-sensor by Affectiva as a prototype. Further more, this study implemented a framework for future research on the basis for developing a low cost, connected, device with Thayer Engineering School at Dartmouth College. With 3 days of data from 10 subjects smoking sessions was detected with just under 90% accuracy using the Conditional Random Field algorithm. However, predicting smoking with Electrodermal Momentary Assessment (EMA) remains an unanswered ques- tion. Hopefully a tool has been provided as a platform for better understanding of habits and behaviour.
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Salvo, Pietro [Verfasser]. "Development of wearable sensors to measure sweat rate and conductivity / Pietro Salvo." München : GRIN Verlag GmbH, 2012. http://d-nb.info/1066507783/34.
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Dang, Wenting. "Stretchable interconnects for smart integration of sensors in wearable and robotic applications." Thesis, University of Glasgow, 2018. http://theses.gla.ac.uk/40994/.
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Stretchable electronic systems are needed in realising a wide range of applications, such as wearable healthcare monitoring where stretching movements are present. Current electronics and sensors are rigid and non-stretchable. However, after integrating with stretchable interconnects, the overall system is able to withstand a certain degree of bending, stretching and twisting. The presence of stretchable interconnects bridges rigid sensors to stretchable sensing networks. In this thesis, stretchable interconnects focusing on the conductive polymer Poly (3,4-ethylenedioxythiophene): poly (4-styrenesulfonate) (PEDOT:PSS) , the composite and the metallic-polyimide (PI) are presented. Three type of stretchable interconnects were developed including gold (Au) -PEDOT:PSS hybrid film interconnects, Graphite-PEDOT:PSS composite interconnects and Au-PI dual-layered interconnects. The Au-PEDOT:PSS hybrid interconnects' stretchability can reach 72%. The composite exhibits a stretchability of 80% but with an extremely high variation in resistance (100000%). The Au-PI interconnects that have a serpentine shape with the arc degree of 260° reveal the highest stretchability, up to 101%, and its resistance variation remains within 0.2%. Further, the encapsulation effect, cyclic stretching, and contact pad's influence, are also investigated. To demonstrate the application of developed stretchable interconnects, this thesis also presents the optimised interconnects integrated with the electrochemical pH sensor and CNT-based strain sensor. The integrated stretchable system with electrochemical pH sensor is able to wirelessly monitor the sweat pH. The whole system can withstand up to 53% strain and more than 500 cycles at 30% strain. For the CNT-based strain sensor, the sensor is integrated on the pneumatically actuated soft robotic finger to monitor the bending radius (23 mm) of the finger. In this way, the movement of the soft robotic finger can be controlled. These two examples of sensor's integration with stretchable interconnects successfully demonstrate the concept of stretchable sensing network. Further work will focus on realising a higher density sensing and higher multifunctional sensing stretchable system seamlessly integrated with cloth fibres.
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Feng, Ziang. "Wearable Power Sources and Self-powered Sensors Based on the Triboelectric Nanogenerators." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/103020.
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The triboelectric nanogenerator (TENG) has attracted global attention in the fields of power sources and self-powered sensors. By coupling the omnipresent triboelectrification effect and the electrical induction effect, the TENGs can transduce ambient mechanical energy into electrical energy. Such energy could be consumed instantaneously or stored for later use. In this way, they could be deployed distributedly to be compatible power sources in the era of the internet of things (IoTs), completing the powering structure that is currently relying on power plants. Also, the electrical signals can reflect the environment changes around the TENGs. Thus, the TENGs can serve as self-powered sensors in the IoTs. In this work, we adopted two approaches for TENG fabrication: the thermal drawing method (TDP) and 3D printing. With TDP, we have fabricated scalable fiber-based triboelectric nanogenerators (FTENG), which have been woven into textiles by an industrial loom for wearable use. This fabrication process can supply FTENG on a large scale and fast speed, bridging the gap between the TENG and weaving industry. With 3D printing, we have fabricated TENGs that are compatible with the shape of arbitrary substrates. They have been used as biocompatible sensors: human-skin-compatible TENG has been used to recognize silent speech in real-time by sensing the chin movement; the porcine-kidney-shaped fiber mesh has been used to monitor the perfusion rate of the organ. These works have extended the territory of TENGs and can be critical components in the IoTs.Ph.D.
Portable electronic devices have become important components in our daily lives, and we are entering the era of the Internet of Things (IoTs), where everyday objects can be interconnected by the internet. While electricity is essential to all of these devices, the traditional power sources are commonly heavy and bulky and need to be recharged or directly connected to the immobile power plants. Researchers have been working to address this mismatch between the device and power systems. The triboelectric nanogenerators (TENG) are good candidates because they can harvest energy in the ambient environment. The users can use them to generate electricity by merely making the rubbing motion. In this work, we report two fabrication methods of the fiber-based triboelectric nanogenerators (FTENG). With the thermal drawing process, we have fabricated sub-kilometer-long FTENG and wove it with the regular cotton yarn into textiles. The wearable power source is human friendly as it does not induce any extra weight load for the user. Besides, we have demonstrated that such long fibers can work as self-powered distributed sensors, such as a Morse code generator. With 3D printing, we have fabricated FTENG-based devices that conform to the working substrates, which can be any shape. We have employed them as biofriendly sensors to translate the chin movement during speaking to language and to monitor the perfusion rate of a pig kidney. The FTENGs have offered excellent comfortability to the users and can play a vital role in reframing the power structure to be compatible with IoTs.
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Redhouse, Amanda Jean. "Joint Angle Estimation Method for Wearable Human Motion Capture." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103629.
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This thesis presents a method for estimating the positions of human limbs during motion that can be applied to wearable, textile-based sensors. The method was validated for the elbow and shoulder joints with data from two garments with resistive, thread-based sensors sewn into the garments at multiple locations. The proposed method was able to estimate the elbow joint position with an average error of 2.2 degrees. The method also produced an average difference in Euclidean distance of 3.7 degrees for the estimated shoulder joint position using data from nine sensors placed around the subject's shoulder. The most accurate combination of sensors on the shoulder garment was found to produce an average difference in distance of 3.4 degrees and used only six sensors. The characteristics of the resistive, thread-based sensor used to validate the method are also detailed as some of their behaviors proved to affect the accuracy of the method negatively.Master of Science
Human motion capture systems gather data on the position of the human body during motion. The data is then used to recreate and analyze the motion digitally. There is a need for motion capture devices capable of measuring long-term data on human motion, especially in physical therapy. However, the currently available motion capture systems have limitations that make long-term or daily use either impossible or uncomfortable. This thesis presents a method that uses data from wearable, textile-based sensors to estimate the positions of human limbs during motion. Two garments were used to validate the method on the elbow and shoulder joints. The proposed method was able to measure the elbow and shoulder joints with an average accuracy that is within the acceptable range for clinical settings.
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Purkis, Tamsin Leigh. "Development and Validation of the Pre- and Post-Processing Algorithms for Quantitative Gait Analysis using a Prototype Wearable Sensor System." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/64317.
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Walking is the most common form of human locomotion and the systematic study thereof is known as gait analysis. Measurement and assessment thereof have application in many fields including clinical diagnosis, rehabilitation and biomechanics. The process of gait evaluation is typically done using an optical motion analysis system combined with stationary force platforms. This is considered the gold standard, but unfortunately, has several drawbacks. It is expensive, requires dedicated laboratories with spatial restrictions, calls for lengthy set up and post-processing times and cannot be used in 'real-world' environments. Alternative systems based on wearable sensors have been developed to overcome these limitations.
The Council for Scientific and Industrial Research (CSIR) has therefore developed a prototype wearable sensor unit consisting of an inertial measurement unit (IMU). The objective of the current study is, therefore, to advance the prototype to a wearable multi-sensor system for quantitative gait analysis. The focus is on the development of the pre- and post-processing algorithms and methods used to transform the measurements into interpretable information.
The focus outlined includes establishing techniques for synchronising the data from the sensors offline, pre-processing the signals, developing algorithms for stride and gait event detection, selecting an appropriate gait model and defining methods for estimating gait parameters. The determined parameters were the spatio-temporal and joint kinematics (hip, knee and ankle). The algorithms and new system were validated against the Vicon motion capture system through gait analyses. The twenty able-bodied volunteers that took part were required to walk across the laboratory six times at three self-selected walking speeds (slow, normal and fast). For the sake of simplicity and due to various limitations, only data in the sagittal plane of the right lower limb of each volunteer was used to validate the wearable system and associated algorithms.
The results obtained were then evaluated against several validation criteria. The absolute mean difference between the estimated timing of detected gait events of the two systems was consistently small (between 0.021 and 7.25% of the gait cycle overall). The spatially dependent parameters, stride length and walking speed, had significant maximum mean absolute percentage errors (31.9 and 34.5% respectively), but with little variation. Excluding outliers, that of the temporal parameters, stride time and cadence, was significantly lower (5.7 and 5.6% respectively). The kinematic results were substantially comparable with a minimum correlation co-efficient of 0.86 and a maximum RMSE of 7.8 degrees with little variation implying repeatability.
Although there were some discrepancies between the outputs, the wearable sensor system and its corresponding algorithms were considered feasible and potentially beneficial to developing countries like South Africa. Recommendations for future work include synchronising data between the wearable and reference system for stride-to-stride comparisons and validating algorithms using a known reliable wearable system.Dissertation (MEng)--University of Pretoria, 2017.
Mechanical and Aeronautical Engineering
MEng
Unrestricted
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McGinnis, Ellen, Ryan McGinnis, Jessica Hruschak, Emily Bilek, Ka Ip, Diana Morelen, Jamie Lawler, Kate Fitzgerald, Katherine Rosenblum, and Maria Musik. "Wearable Sensors Outperform Behavioral Coding as Valid Marker of Childhood Anxiety and Depression." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etsu-works/7702.
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There is a significant need to develop objective measures for identifying children under the age of 8 who have anxiety and depression. If left untreated, early internalizing symptoms can lead to adolescent and adult internalizing disorders as well as comorbidity which can yield significant health problems later in life including increased risk for suicide. To this end, we propose the use of an instrumented fear induction task for identifying children with internalizing disorders, and demonstrate its efficacy in a sample of 63 children between the ages of 3 and 7. In so doing, we extract objective measures that capture the full six degree-of-freedom movement of a child using data from a belt-worn inertial measurement unit (IMU) and relate them to behavioral fear codes, parent-reported child symptoms and clinician-rated child internalizing diagnoses. We find that IMU motion data, but not behavioral codes, are associated with parent-reported child symptoms and clinician-reported child internalizing diagnosis in this sample. These results demonstrate that IMU motion data are sensitive to behaviors indicative of child psychopathology. Moreover, the proposed IMU-based approach has increased feasibility of collection and processing compared to behavioral codes, and therefore should be explored further in future studies.
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Prahl, Anne. "Designing wearable sensors for Preventative Health : an exploration of material, form and function." Thesis, University of the Arts London, 2015. http://ualresearchonline.arts.ac.uk/9077/.
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The financial burden on global healthcare systems has reached unprecedented levels and as a result, attention has been shifting from the traditional approach of disease management and treatment towards prevention (Swan, 2012). Wearable devices for Preventative Health have become a focus for innovation across academia and industry, thus this thesis explores the design of wearable biochemical and environmental sensors, which can provide users with an early warning, detection and monitoring system that could integrate easily into their existing lives. The research aims to generate new practical knowledge for the design and development of wearable sensors and, motivated by the identification of compelling design opportunities, merges three strands of enquiry. The research methodology supports this investigation into material, form and function through the use of key practice-based methods, which include Participatory Action Research (active immersion and participation in a particular community and user workshops) and the generation and evaluation of a diverse range of artefacts. Based on the user-centred investigation of the use case for biochemical and environmental sensing, the final collection of artefacts demonstrates a diverse range of concepts, which present biodegradable and recyclable nonwoven material substrates for the use in non-integrated sensors. These sensors can be skin-worn, body-worn or clothing-attached for in-situ detection and monitoring of both internal (from the wearer) and external (from the environment) stimuli. The research proposes that in order to engage a broad section of the population in a preventative lifestyle to significantly reduce the pressure on global healthcare systems, wearable sensors need to be designed so they can appeal to as many users as possible and integrate easily into their existing lifestyles, routines and outfits. The thesis argues that this objective could be achieved through the design and development of end-of-life considered and cost-effective substrate materials, non-integrated wearable form factors and meticulous consideration of a divergent range of user needs and preferences, during the early stages of design practice.
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Perumal, Shyam Vignesh. "Gait and Tremor Monitoring System for Patients with Parkinson’s Disease Using Wearable Sensors." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6353.
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Typically, a Parkinson’s disease (PD) patient would display instances of tremor and bradykinesia (slowness of movement) at an early stage of the disease and later develop gait disturbances and postural instability. So, it is important to measure the tremor occurrences in subjects to detect the onset of PD. Also, it is equally essential to monitor the gait impairments that the patient displays, as the order at which the PD symptoms appear in subjects vary from one to another.
The primary goal of this thesis is to develop a monitoring system for PD patients using wearable sensors. To achieve that objective, our work focused first on identifying the most significant features that would best distinguish between PD and normal healthy subjects. Here, the various gait and tremor features were extracted from the raw data collected from the wearable sensors and further analyzed using statistical analysis and pattern classification techniques to pick the most significant features. In statistical analysis, the analysis of variance (ANOVA) test was conducted to differentiate the subjects based on the values of the mean. Further, pattern classification was carried out using the Linear Discriminant Analysis (LDA) algorithm. The analysis of our results shows that the features of heel force, step distance, stance and swing phases contributed more significantly to achieving a better classification between a PD and a normal subject, in comparison with other features. Moreover, the tremor analysis based on the frequency-domain characteristics of the signal including amplitude, power distribution, frequency dispersion, and median frequency was carried out to identify PD tremor from different types of artifacts.
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Sole, C. J., Caleb D. Bazyler, Ashley A. Kavanaugh, Satoshi Mizuguchi, and Michael H. Stone. "Relationship between Internal and External Estimates of Training Load Using Wearable Inertial Sensors." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etsu-works/3837.
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PURPOSE: The purpose of the study was to examine the relationship between an external estimate of training load obtained from a wearable accelerometer device and perceived training load in women’s volleyball. METHODS: Participants of this study were thirteen NCAA Division I women’s volleyball players (Age: 20.3±1.2 y, height: 174.9±7.9cm, body mass: 68.1±12.7 kg). A wearable accelerometer device (Catapult Sports, MiniMaxX S4) was used to estimate external training load during volleyball practice sessions. In addition, following each session a rating of perceived exertion was obtained from each player using a 0-10 scale. Based on previously established methods, ratings of perceived exertion were then multiplied by the duration of practice in minutes to provide an estimate of internal training load. A Pearson product-moment zero order correlation coefficient was used to assess the relationship between external and internal training load estimates for each individual over eight practices. RESULTS: On average a positive relationship (r = 0.75±0.15) was found between training load estimates. Individual r values ranged from 0.39 to 0.92, with eight of the thirteen achieving statistical significance (p<0.05). CONCLUSIONS: Based on the relationships found between internal and external estimates of training load, both methods may be considered as an option for quantifying on-court training loads in NCAA women’s volleyball. However, the degree to which these estimates relate may vary by individual.
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Trojaniello, Diana <1986>. "Assessment of gait spatio-temporal parameters in neurological disorders using wearable inertial sensors." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/7122/1/Trojaniello_Diana_tesi.pdf.
Full textAbstract:
Movement analysis carried out in laboratory settings is a powerful, but costly solution since it requires dedicated instrumentation, space and personnel. Recently, new technologies such as the magnetic and inertial measurement units (MIMU) are becoming widely accepted as tools for the assessment of human motion in clinical and research settings. They are relatively easy-to-use and potentially suitable for estimating gait kinematic features, including spatio-temporal parameters.
The objective of this thesis regards the development and testing in clinical contexts of robust MIMUs based methods for assessing gait spatio-temporal parameters applicable across a number of different pathological gait patterns.
First, considering the need of a solution the least obtrusive as possible, the validity of the single unit based approach was explored. A comparative evaluation of the performance of various methods reported in the literature for estimating gait temporal parameters using a single unit attached to the trunk first in normal gait and then in different pathological gait conditions was performed.
Then, the second part of the research headed towards the development of new methods for estimating gait spatio-temporal parameters using shank worn MIMUs on different pathological subjects groups. In addition to the conventional gait parameters, new methods for estimating the changes of the direction of progression were explored. Finally, a new hardware solution and relevant methodology for estimating inter-feet distance during walking was proposed.
Results of the technical validation of the proposed methods at different walking speeds and along different paths against a gold standard were reported and showed that the use of two MIMUs attached to the lower limbs associated with a robust method guarantee a much higher accuracy in determining gait spatio-temporal parameters.
In conclusion, the proposed methods could be reliably applied to various abnormal gaits obtaining in some cases a comparable level of accuracy with respect to normal gait.
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Trojaniello, Diana <1986>. "Assessment of gait spatio-temporal parameters in neurological disorders using wearable inertial sensors." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/7122/.
Full textAbstract:
Movement analysis carried out in laboratory settings is a powerful, but costly solution since it requires dedicated instrumentation, space and personnel. Recently, new technologies such as the magnetic and inertial measurement units (MIMU) are becoming widely accepted as tools for the assessment of human motion in clinical and research settings. They are relatively easy-to-use and potentially suitable for estimating gait kinematic features, including spatio-temporal parameters.
The objective of this thesis regards the development and testing in clinical contexts of robust MIMUs based methods for assessing gait spatio-temporal parameters applicable across a number of different pathological gait patterns.
First, considering the need of a solution the least obtrusive as possible, the validity of the single unit based approach was explored. A comparative evaluation of the performance of various methods reported in the literature for estimating gait temporal parameters using a single unit attached to the trunk first in normal gait and then in different pathological gait conditions was performed.
Then, the second part of the research headed towards the development of new methods for estimating gait spatio-temporal parameters using shank worn MIMUs on different pathological subjects groups. In addition to the conventional gait parameters, new methods for estimating the changes of the direction of progression were explored. Finally, a new hardware solution and relevant methodology for estimating inter-feet distance during walking was proposed.
Results of the technical validation of the proposed methods at different walking speeds and along different paths against a gold standard were reported and showed that the use of two MIMUs attached to the lower limbs associated with a robust method guarantee a much higher accuracy in determining gait spatio-temporal parameters.
In conclusion, the proposed methods could be reliably applied to various abnormal gaits obtaining in some cases a comparable level of accuracy with respect to normal gait.
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Zambrano, Ericsson Ocas, Kemeli Reyes Munoz, Jimmy Armas-Aguirre, and Paola A. Gonzalez. "Technological Architecture with Low Cost Sensors to Improve Physical Therapy Monitoring." IEEE Computer Society, 2020. http://hdl.handle.net/10757/656576.
Full textAbstract:
El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.In this article, we propose a wireless monitoring solution for gait parameters using low-cost sensors in the physical rehabilitation of patients with gait disorders. This solution consists of infrared speed sensors (IRSS), force-sensing Resistor (FSR) and microcontrollers placed in a walker. These sensors collect the pressure distribution on the walker's handle and the speed of the steps during therapy session. The proposal allows to improve the traditional physiotherapy session times through a mobile application to perform the monitoring controlled by a health specialist in real time. The proposed solution consists of 4 stages: 1. Obtaining gear parameters, 2. Data transmission, 3. Information Storage and 4. Data collection and processing. Solution was tested with 10 patients from a physical rehabilitation center in Lima, Peru. Preliminary results revealed a significant reduction in the rehabilitation session from 25 to 5.2 minutes.
Revisión por pares
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Siirtola, P. (Pekka). "Recognizing human activities based on wearable inertial measurements:methods and applications." Doctoral thesis, Oulun yliopisto, 2015. http://urn.fi/urn:isbn:9789526207698.
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Abstract Inertial sensors are devices that measure movement, and therefore, when they are attached to a body, they can be used to measure human movements. In this thesis, data from these sensors are studied to recognize human activities user-independently. This is possible if the following two hypotheses are valid: firstly, as human movements are dissimilar between activities, also inertial sensor data between activities is so different that this data can be used to recognize activities. Secondly, while movements and inertial data are dissimilar between activities, they are so similar when different persons are performing the same activity that they can be recognized as the same activity. In this thesis, pattern recognition -based solutions are applied to inertial data to find these dissimilarities and similarities, and therefore, to build models to recognize activities user-independently. Activity recognition within this thesis is studied in two contexts: daily activity recognition using mobile phones, and activity recognition in industrial context. Both of these contexts have special requirements and these are considered in the presented solutions. Mobile phones are optimal devices to measure daily activity: they include a wide range of useful sensors to detect activities, and people carry them with them most of the time. On the other hand, the usage of mobile phones in active recognition includes several challenges; for instance, a person can carry a phone in any orientation, and there are hundreds of smartphone models, and each of them have specific hardware and software. Moreover, as battery life is always as issue with smartphones, techniques to lighten the classification process are proposed. Industrial context is different from daily activity context: when daily activities are recognized, occasional misclassifications may disturb the user, but they do not cause any other type of harm. This is not the case when activities are recognized in industrial context and the purpose is to recognize if the assembly line worker has performed tasks correctly. In this case, false classifications may be much more harmful. Solutions to these challenges are presented in this thesis. The solutions introduced in this thesis are applied to activity recognition data sets. However, as the basic idea of the activity recognition problem is the same as in many other pattern recognition procedures, most of the solutions can be applied to any pattern recognition problem, especially to ones where time series data is studiedTiivistelmä Liikettä mittaavista antureista, kuten kiihtyvyysantureista, saatavaa tietoa voidaan käyttää ihmisten liikkeiden mittaamiseen kiinnittämällä ne johonkin kohtaan ihmisen kehoa. Väitöskirjassani tavoitteena on opettaa tähän tietoon perustuvia käyttäjäriippumattomia malleja, joiden avulla voidaan tunnistaa ihmisten toimia, kuten käveleminen ja juokseminen. Näiden mallien toimivuus perustuu seuraavaan kahteen oletukseen: (1) koska henkilöiden liikkeet eri toimissa ovat erilaisia, myös niistä mitattava anturitieto on erilaista, (2) useamman henkilön liikkeet samassa toimessa ovat niin samanlaisia, että liikkeistä mitatun anturitiedon perusteella nämä liikkeet voidaan päätellä kuvaavan samaa toimea. Tässä väitöskirjassa käyttäjäriippumaton ihmisten toimien tunnistus perustuu hahmontunnistusmenetelmiin ja tunnistusta on sovellettu kahteen eri asiayhteyteen: arkitoimien tunnistamiseen älypuhelimella sekä toimintojen tunnistamiseen teollisessa ympäristössä. Molemmilla sovellusalueilla on omat erityisvaatimuksensa ja -haasteensa. Älypuhelimien liikettä mittaavien antureihin perustuva tunnistus on haastavaa esimerkiksi siksi, että puhelimen asento ja paikka voivat vaihdella. Se voi olla esimerkiksi laukussa tai taskussa, lisäksi se voi olla missä tahansa asennossa. Myös puhelimen akun rajallinen kesto luo omat haasteensa. Tämän vuoksi tunnistus tulisi tehdä mahdollisimman kevyesti ja vähän virtaa kuluttavalla tavalla. Teollisessa ympäristössä haasteet ovat toisenlaisia. Kun tarkoituksena on tunnistaa esimerkiksi työvaiheiden oikea suoritusjärjestys kokoamislinjastolla, yksikin virheellinen tunnistus voi aiheuttaa suuren vahingon. Teollisessa ympäristössä tavoitteena onkin tunnistaa toimet mahdollisimman tarkasti välittämättä siitä kuinka paljon virtaa ja tehoa tunnistus vaatii. Väitöskirjassani kerrotaan kuinka nämä erityisvaatimukset ja -haasteet voidaan ottaa huomioon suunniteltaessa malleja ihmisten toimien tunnistamiseen. Väitöskirjassani esiteltyjä uusia menetelmiä on sovellettu ihmisten toimien tunnistamiseen. Samoja menetelmiä voidaan kuitenkin käyttää monissa muissa hahmontunnistukseen liittyvissä ongelmissa, erityisesti sellaisissa, joissa analysoitava tieto on aikasarjamuotoista
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Possanzini, Luca. "Mechanical and electrical characterization of wearable textile pressure and strain sensors based on PEDOT:PSS." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14801/.
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Il termine tecnologia indossabile si riferisce a quei dispositivi elettronici incorporati negli indumenti od accessori che possono essere comodamente indossati.
Essi sono ampiamente utilizzati in campo medico, sportivo, educativo o per monitorare disabilità.
In questa tesi sono stati sviluppati sensori di pressione e di deformazione tessili, proponendo il modello teorico che ne descrive il comportamento.
L'elemento attivo di tali sensori tessili è basato sul polimero intrinsecamente conduttivo (PEDOT:PSS).
La soluzione conduttiva è stata depositata sui tessuti tramite il metodo drop-casting e la tecnica screen printing.
La teoria sviluppata per il tessuto di cotone ha dimostrato che è possibile cambiare il range di pressione in cui i sensori rispondono cambiando la concentrazione di glicole etilenico presente nella soluzione di PEDOT:PSS pur mantenendo la geometria dei sensori inalterata.
Per realizzare un'applicazione reale, il sensore di pressione tessile è stato fabbricato su un tessuto tecnico sportivo elastico. Comportamenti simili sono stati ottenuti dimostrando la validità del modello proposto.
Successivamente, sono presentati i processi di fabbricazione e la caratterizzazione elettro-meccanica di sensori di deformazione tessili.
Range tests e stability tests eseguiti su questi sensori di deformazione forniscono notizie circa le loro prestazioni:affidabilità e gauge factor.
Il meccanismo di rilevamento è stato analizzato con un modello teorico basato sulle proprietà del tessuto e sulla deformazione della struttura wale-course tipica dei tessuti a maglia.
I risultati ottenuti durante questo lavoro permettono lo sviluppo di una nuova generazione di sensori di pressione e di deformazione tessili che potranno essere comodamente indossati nella vita di tutti i giorni.
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Huynh, Duy Tâm Gilles. "Human Activity Recognition with Wearable Sensors." Phd thesis, 2008. https://tuprints.ulb.tu-darmstadt.de/1132/1/Dissertation_Tam_Huynh.pdf.
Full textAbstract:
This thesis investigates the use of wearable sensors to recognize human activity. The activity of the user is one example of context information -- others include the user's location or the state of his environment -- which can help computer applications to adapt to the user depending on the situation. In this thesis we use wearable sensors -- mainly accelerometers -- to record, model and recognize human activities. Using wearable sensors allows continuous recording of activities across different locations and independent from external infrastructure. There are many possible applications for activity recognition with wearable sensors, for instance in the areas of healthcare, elderly care, personal fitness, entertainment, or performing arts. In this thesis we focus on two particular research challenges in activity recognition, namely the need for less supervision, and the recognition of high-level activities. We make several contributions towards addressing these challenges. Our first contribution is an analysis of features for activity recognition. Using a data set of activities such as walking, standing, sitting, or hopping, we analyze the performance of commonly used features and window lengths over which the features are computed. Our results indicate that different features perform well for different activities, and that in order to achieve best recognition performance, features and window lengths should be chosen specific for each activity. In order to reduce the need for labeled training data, we propose an unsupervised algorithm which can discover structure in unlabeled recordings of activities. The approach identifies correlated subsets in feature space, and represents these subsets with low-dimensional models. We show that the discovered subsets often correspond to distinct activities, and that the resulting models can be used for recognition of activities in unknown data. In a separate study, we show that the approach can be effectively deployed in a semi-supervised learning framework. More specifically, we combine the approach with a discriminant classifier, and show that this scheme allows high recognition rates even when using only a small amount of labeled training data. Recognition of higher-level activities such as shopping, doing housework, or commuting is challenging, as these activities are composed of changing sub-activities and vary strongly across individuals. We present one study in which we recorded 10h of three different high-level activities, investigating to which extent methods for low-level activities can be scaled to the recognition of high-level activities. Our results indicate that for settings as ours, traditional supervised approaches in combination with data from wearable accelerometers can achieve recognition rates of more than 90%. While unsupervised techniques are desirable for short-term activities, they become crucial for long-term activities, for which annotation is often impractical or impossible. To this end we propose an unsupervised approach based on topic models that allows to discover high-level structure in human activity data. The discovered activity patterns correlate with daily routines such as commuting, office work, or lunch routine, and they can be used to recognize such routines in unknown data.
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Liu, Fang Ting, and 劉芳廷. "Gesture Recognition with Wearable 9-axis Sensors." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/k5afp4.
Full textAbstract:
碩士國立清華大學
電機工程學系
105
Gesture recognition is a topic in computer science with the goal of describing human gestures through mathematical algorithms in recent year. In the field of hand gesture recognition,it apply in many kinds of technologies such as mobile phone applications, wearable wireless devices, sports detection, video game or art combination. In this thesis, we will record signals of eight kinds of hand movements into computer using wearable wireless device with nine axis sensor (including accelerometer, gyroscope and magnetometer) worn on the wrist, then recognized gestures using the algorithms being described later. We built a system of recognition with machine learning classification process. Besides classification process, we also developed a thresholding method to easily detect movements. In the thresholding method, for each movement, we defined threshold value for each kind of data and filtered the movements data with threshold combined with detection windows. However, not all the movements can be detected by this easy and less calculation method so that we finally used a machine learning process to solve problems. The analyzing of the two method will be introduced later. In order to achieve higher recognition accuracy, we used machine learning process in the system and did feature extraction to get well distinguished features. We used principal component analysis (PCA) and linear discriminant analysis (LDA) to extract features. The advantages of PCA and LDA are reducing dimensions of data while preserving as much of the class discriminatory information as possible and reducing the training time of classification. Last, with support vector machine (SVM), we can recognize movement with higher accuracy with less computation time, and it also support data with high dimension. We can model even non-linear relations with more precise classification due to SVM kernels. In our experiment, we can get the accuracy of recognition at 99.63% for 8 classes with 20 subjects data for 5 times each in user-dependent case, and 12 subjects testing data for user-independent case with recognition rate at 88.43%.
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Tomé, Ana Filipa Soares. "Soft Sensors for Soft and Wearable Robots." Master's thesis, 2014. http://hdl.handle.net/10316/28014.
Full textAbstract:
Dissertação de Mestrado Integrado em Engenharia Biomédica apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraWith a growing need for safer human-robot interactions and taking the human skin properties as biological inspiration, the technological field of soft sensors has grown considerably in the last few years. Conventional tactile sensors integrated on robot gripers are nowadays available in multiple formats. Nonetheless, most of the tasks performed by robots demand complex rotational movements. Sensors adaptable to their host can turn robotic devices into safer technology to interact with and are easier to integrate in wearable devices since they do not interfere with their mechanical performance. The ease of access to soft materials and fabrication methods of customized objects through 3D printing, allows the development of soft sensors with desired geometries using low cost and simple methods. This work addresses the development of two distinct soft sensors, with embedded liquid-metal microchannels, by casting a liquid elastomer into 3D printed molds engraved with micro-dimensioned features. The first type of stretchable sensor, was designed for strain-sensing and can be applied in multiple devices to give information about joint angles and posture of prosthetic hands. The second designed sensor is intended to detect contact forces during manipulation and assembly. This project is a good example of how a mix of multidisciplinary knowledge coming from materials engineering, electronics, and robotics can form the basis of engineering state-of-art devices which can contribute to the further study and development of artificial skins with multiple sensing capabilities.
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林世祐. "Action Surveillance Using Sparse Wearable Inertial Sensors." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/50200252692004396811.
Full textAbstract:
碩士國立交通大學
資訊科學與工程研究所
101
Motion reconstruction from sensor data is a notable research field. In this thesis, we present a framework to reconstruct full-body human motion by four to five inertial sensors that attached to the user’s four limbs and torso. Based on the gathered data, we construct an online k-dimensional tree (kd-tree) index structure which consists of hundred thousands of frames, and find the most appropriate motion fragment as user’s current full-body motion. However, the sparse and noisy sensing data cause high ambiguity for our motion estimation. It then results in gaps between poses continuous. Consequently, we include the concept of motion fields for more reasonable motion transition. This run-time motion synthesis mechanism merges the candidates of the motion sequences by weighted combination, and generates natural and smooth motions.
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Kedambaimoole, Vaishakh. "Wearable Sensors using Solution Processed 2D Materials." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4920.
Full textAbstract:
Wearable sensors, as the name implies, are devices that can be donned onto the body in order to continuously detect, monitor and analyze various signals generated by the subject and the immediate surroundings. Applications of these sensors span over the vast domains of healthcare, athletics, automation and robotics. Conventional wafer-based electronics are brittle and rigid. Wearable devices demand new materials that provide mechanical liberty in terms of
flexibility and stretchability with superior functionalities. When the physical dimensions of materials are reduced to
the nano scale regime, they exhibit remarkable change in their properties compared to their bulky counterparts. Most widely explored nano materials include 0D, 1D and 2D structures synthesized via advanced processing and chemical routes. The recent progress in nano materials and fabrication methodologies provide new routes to develop sensors that can be bent, stretched, twisted, compressed, or deformed into arbitrary shapes. My research work is focused on creative utilization 2D materials to develop wearable sensors with the aim of providing seamless user experience.
Functionalized inks of 2D materials offer versatile fabrication methods like coating, printing, stamping and patterning for development of flexible sensors that are industrially scalable. Present thesis aims to provide insights into use of graphene and MXene inks for realization of novel wearable devices. Specific focus has been set on integration of solution processed graphene on fabric for e-textile applications, ultrathin graphene-based tattoo sensors for proximity sensing studies and skin conformal MXene tattoo for physiological sensing. As fabrication of next generation sensors for wearable applications pose their own unique challenges, my research work aims to deliver innovative methods to address these issues.
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Chua, Jason Yap Moore Carl A. "Design of a wearable cobot." Diss., 2006. http://etd.lib.fsu.edu/theses/available/etd-03012006-152943.
Full textAbstract:
Thesis (M.S.)--Florida State University, 2006.Advisor: Carl A. Moore, Florida State University, College of Engineering, Dept. of Mechanical Engineering. Title and description from dissertation home page (viewed June 8, 2006). Document formatted into pages; contains x, 107 pages. Includes bibliographical references.