Relevant bibliographies by topics / Wearable Sensors

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Wearable Sensors'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Wearable Sensors"

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Kalupahana, Ayanga Imesha Kumari, Ananta Narayanan Balaji, Xiaokui Xiao, and Li-Shiuan Peh. "SeRaNDiP." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 2 (June 12, 2023): 1–38. http://dx.doi.org/10.1145/3596252.

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Personal data collected from today's wearable sensors contain a rich amount of information that can reveal a user's identity. Differential privacy (DP) is a well-known technique for protecting the privacy of the sensor data being sent to community sensing applications while preserving its statistical properties. However, differential privacy algorithms are computationally expensive, requiring user-level random noise generation which incurs high overheads on wearables with constrained hardware resources. In this paper, we propose SeRaNDiP -- which utilizes the inherent random noise existing in wearable sensors for distributed differential privacy. We show how various hardware configuration parameters available in wearable sensors can enable different amounts of inherent sensor noise and ensure distributed differential privacy guarantee for various community sensing applications with varying sizes of populations. Our evaluations of SeRaNDiP on five wearable sensors that are widely used in today's commercial wearables -- MPU-9250 accelerometer, ADXL345 accelerometer, BMP 388 barometer, MLP 3115A2 barometer, and MLX90632 body temperature sensor show a 1.4X-1.8X computation/communication speedup and 1.2X-1.5X energy savings against state-of-the-art DP implementation. To the best of our knowledge, SeRaNDiP is the first framework to leverage the inherent random sensor noise for differential privacy preservation in community sensing without any hardware modification.
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Yin, Yunlei, Cheng Guo, Hong Li, Hongying Yang, Fan Xiong, and Dongyi Chen. "The Progress of Research into Flexible Sensors in the Field of Smart Wearables." Sensors 22, no. 14 (July 6, 2022): 5089. http://dx.doi.org/10.3390/s22145089.

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In modern society, technology associated with smart sensors made from flexible materials is rapidly evolving. As a core component in the field of wearable smart devices (or ‘smart wearables’), flexible sensors have the advantages of excellent flexibility, ductility, free folding properties, and more. When choosing materials for the development of sensors, reduced weight, elasticity, and wearer’s convenience are considered as advantages, and are suitable for electronic skin, monitoring of health-related issues, biomedicine, human–computer interactions, and other fields of biotechnology. The idea behind wearable sensory devices is to enable their easy integration into everyday life. This review discusses the concepts of sensory mechanism, detected object, and contact form of flexible sensors, and expounds the preparation materials and their applicability. This is with the purpose of providing a reference for the further development of flexible sensors suitable for wearable devices.
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Aroganam, Gobinath, Nadarajah Manivannan, and David Harrison. "Review on Wearable Technology Sensors Used in Consumer Sport Applications." Sensors 19, no. 9 (April 28, 2019): 1983. http://dx.doi.org/10.3390/s19091983.

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This review paper discusses the trends and projections for wearable technology in the consumer sports sector (excluding professional sport). Analyzing the role of wearable technology for different users and why there is such a need for these devices in everyday lives. It shows how different sensors are influential in delivering a variety of readings that are useful in many ways regarding sport attributes. Wearables are increasing in function, and through integrating technology, users are gathering more data about themselves. The amount of wearable technology available is broad, each having its own role to play in different industries. Inertial measuring unit (IMU) and Global Positioning System (GPS) sensors are predominantly present in sport wearables but can be programmed for different needs. In this review, the differences are displayed to show which sensors are compatible and which ones can evolve sensor technology for sport applications.
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Kandpal, Jyoti. "Exploring the Potential of Wearable Electronics for Healthcare Monitoring and Diagnosis." Mathematical Statistician and Engineering Applications 71, no. 2 (March 6, 2022): 658–69. http://dx.doi.org/10.17762/msea.v71i2.2195.

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Chronic diseases kill many Humans in all over the world. Monitor risk factors including physical exercise to manage these illnesses. Wearables like Fitbit can track and give health data to help users make decisions. Most wearables marketing targets the young, active, and most populous racial groups. Wearable electronics can revolutionize healthcare by continuously monitoring health factors. Sensor technology, data processing, and communication protocols have made wearable gadgets useful for healthcare monitoring and diagnosis. This article discusses sensors, data processing, and communication protocols used in wearable electronics to revolutionize healthcare monitoring and diagnosis. A side-by-side table compares each method's pros and cons. The topic covers wearable electronics processing for healthcare monitoring and diagnosis. A block architecture and graphic explain healthcare monitoring and diagnosis using wearable electronics. Wearable electronics adoption is often hampered by concerns regarding data privacy and security, data reliability, and healthcare system compatibility. Wearable electronics are revolutionizing medicine in numerous ways, from monitoring chronic illnesses to giving emergency treatment. Wearable tech could develop into artificial intelligence, machine learning, augmented reality, virtual reality, cutting-edge sensors, telemedicine, 5G networks, nanotechnology, and blockchain. Finally, wearable electronics research could improve patient outcomes and quality of life, transforming healthcare.
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Wu, Chenggen, Xun Zhang, Rui Wang, Li Jun Chen, Meng Nie, Zhiqiang Zhang, Xiaodong Huang, and Lei Han. "Low-dimensional material based wearable sensors." Nanotechnology 33, no. 7 (November 25, 2021): 072001. http://dx.doi.org/10.1088/1361-6528/ac33d1.

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Abstract Wearable sensors are believed to be the most important part of the Internet of Things. In order to meet the application requirements, low-dimensional materials such as graphene and carbon nanotubes have been attempted to constitute wearable sensors with high performance. Our discussions in this review include the different low-dimensional material based sensors which are employed in wearable applications. Low-dimensional materials based wearable sensors for detecting various physical quantities in surroundings, including temperature sensor, pressure or strain sensor and humidity sensor, is introduced. The primary objective of this paper is to provide a comprehensive review of research status and future development direction of low-dimensional materials based wearable sensors. Challenges for developing commercially low-dimensional namomaterials based wearable sensors are highlighted as well.
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Ozanich, Richard. "Chem/bio wearable sensors: current and future direction." Pure and Applied Chemistry 90, no. 10 (October 25, 2018): 1605–13. http://dx.doi.org/10.1515/pac-2018-0105.

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AbstractExamples of existing and emerging wearable sensors for chemical and biological threat agents are reviewed and essential enabling developments identified. Wearables are described as inward looking sensors (self-monitoring) and outward looking sensors (environmental sensors). The future potential for wearable sensors, expected capabilities, and key challenges are summarized.
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Chen, Hui, Han Wang, Peilun Yu, and Xiaoyang Yang. "Wearable Strain Sensors and Their Applications." SHS Web of Conferences 157 (2023): 03029. http://dx.doi.org/10.1051/shsconf/202315703029.

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Wearable and stretchable strain sensors have received much attention because of their easy interaction with the human body. They are widely used in many fields, such as healthcare monitoring and human motion detection. Recent advances in the design and implementation of wearable and stretchable strain sensors and their application prospects are summarized herein. The research on sensitive strain sensors will be introduced herein first, which mainly involves the application of nanomaterials in the strain sensor. The remarkable properties of nanomaterials enable the carbon nanotube sensor to be embedded in socks, gloves, bandages, and other items that can be attached to the human body to accurately monitor various movements of the human body, including training, breathing, typing, and speaking. And then, we will focus on the application prospects of wearable strain sensors. With the development of the Times and the progress of science and technology, wearable strain sensors are gradually applied to various fields, especially in intelligent medical treatment, sports and fitness, and entertainment. Although the research on wearable strain sensor has produced considerable progress so far, it is still in the prototype stage, and wearable strain sensor still faces significant challenges in manufacturing a multi-functional integrated strain sensor. The research of this paper will be of great value to the study and application of wearable strain sensors.
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AMAlANATHAN, SELVIA AM, ABDULAZIZ ASIRI, and AMER AL ALI. "Mental Health Prediction Using Artificial Intelligence- Machine Learning: Pain and Stress Detection Using Wearable Sensors and Devices—A Review." YMER Digital 21, no. 08 (August 12, 2022): 528–42. http://dx.doi.org/10.37896/ymer21.08/45.

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Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This re- view presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin tempera- ture) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue. Keywords: Mental health, machine learning, pain detection; stress detection; wearable sensor; physiological signals; behavioral signals
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Kim, SangUn, TranThuyNga Truong, JunHyuk Jang, and Jooyong Kim. "The Programmable Design of Large-Area Piezoresistive Textile Sensors Using Manufacturing by Jacquard Processing." Polymers 15, no. 1 (December 25, 2022): 78. http://dx.doi.org/10.3390/polym15010078.

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Among wearable e-textiles, conductive textile yarns are of particular interest because they can be used as flexible and wearable sensors without affecting the usual properties and comfort of the textiles. Firstly, this study proposed three types of piezoresistive textile sensors, namely, single-layer, double-layer, and quadruple-layer, to be made by the Jacquard processing method. This method enables the programmable design of the sensor’s structure and customizes the sensor’s sensitivity to work more efficiently in personalized applications. Secondly, the sensor range and coefficient of determination showed that the sensor is reliable and suitable for many applications. The dimensions of the proposed sensors are 20 × 20 cm, and the thicknesses are under 0.52 mm. The entire area of the sensor is a pressure-sensitive spot. Thirdly, the effect of layer density on the performance of the sensors showed that the single-layer pressure sensor has a thinner thickness and faster response time than the multilayer pressure sensor. Moreover, the sensors have a quick response time (<50 ms) and small hysteresis. Finally, the hysteresis will increase according to the number of conductive layers. Many tests were carried out, which can provide an excellent knowledge database in the context of large-area piezoresistive textile sensors using manufacturing by Jacquard processing. The effects of the percolation of CNTs, thickness, and sheet resistance on the performance of sensors were investigated. The structural and surface morphology of coating samples and SWCNTs were evaluated by using a scanning electron microscope. The structure of the proposed sensor is expected to be an essential step toward realizing wearable signal sensing for next-generation personalized applications.
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Dumais, Kelly, Adam Jagodinsky, Saima Khakwani, Rebecca Bonaker, Bryan McDowell, and Kristen Sowalsky. "Abstract PO5-11-12: The use of wearable sensors and patient-reported outcomes in breast cancer research: A literature survey." Cancer Research 84, no. 9_Supplement (May 2, 2024): PO5–11–12—PO5–11–12. http://dx.doi.org/10.1158/1538-7445.sabcs23-po5-11-12.

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Abstract Background: Clinical outcome assessments (COAs) related to physical activity, sleep, and functional mobility (gait and balance) are common in breast cancer research as they provide insight into treatment effects and overall quality of life. Wearable sensors offer utility in supplementing traditional COAs by providing objective data by passive, continuous measurement, thereby gaining unique insight on functioning while reducing patient burden. However, a comprehensive understanding of how wearables are being used in breast cancer research and how they correlate with subjective measurement of functioning is lacking. Our aim was to identify how wearable sensors and patient-reported outcomes (PROs) are being utilized in breast cancer research, the common areas of overlap, as well as areas in which expanded use of wearable sensors may be beneficial in clinical breast cancer research. Methods: We conducted a non-systematic survey of breast cancer literature using electronic databases (PubMed, Web of Science, Elsevier and Clinicaltrials.gov) to identify oncology trials using both wearable sensors and PROs. Details such as PROs used, types of sensors used, and the data collected from these were extracted and summarized. There were no restrictions placed on the date and year of publication. Results: A total of 15 breast cancer studies, which used both wearable sensors and PROs to measure patient outcomes, were included in analysis. Accelerometers were implemented most frequently in the studies analyzed (73%) followed by pedometers (40%). The most common outcomes measured using wearable sensors were physical activity (47%) and sleep (47%), with only 1 study (7%) measuring functional mobility(gait and balance). The most common outcomes measured using PROs were sleep (47%), followed by quality of life (40%), physical activity (33%), and “other” parameters (40%; e.g., mood, fatigue, pain). Correlation between PRO and sensor metrics were sparce (20%), yet sensor measures of physical activity, sleep, and gait/balance correlated respectively with PROs of activity, sleep, and quality of life. Conclusions: Sleep, quality of life, and physical activity were the most common outcomes measured by PROs, while physical activity and sleep were the most common outcomes captured using wearable sensors. These results suggest there is alignment in outcomes gathered from wearable sensors and PROs, however additional insight may be gained by incorporating wearable sensors for assessment of functional mobility (e.g. walk or balance tests) as these areas are known to impact quality of life in cancer patients and cancer survivors. Further, few studies analyzed correlations between the two measurement systems, suggesting a greater need in understanding how objective measures via wearable sensors and subjective measurements relate. Citation Format: Kelly Dumais, Adam Jagodinsky, Saima Khakwani, Rebecca Bonaker, Bryan McDowell, Kristen Sowalsky. The use of wearable sensors and patient-reported outcomes in breast cancer research: A literature survey [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-11-12.
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Dissertations / Theses on the topic "Wearable Sensors"

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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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Books on the topic "Wearable Sensors"

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Mukhopadhyay, Subhas C., ed. Wearable Electronics Sensors. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18191-2.

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Lee, James, Keane Wheeler, and Daniel A. James. Wearable Sensors in Sport. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3777-2.

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Yang, Canjun, G. S. Virk, and Huayong Yang, eds. Wearable Sensors and Robots. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2404-7.

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Gupta, Ram K. Flexible and Wearable Sensors. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003299455.

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James, Daniel A., and Nicola Petrone. Sensors and Wearable Technologies in Sport. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0992-1.

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D, Lara Yejas Oscar, ed. Human activity recognition: Using wearable sensors and smartphones. Boca Raton: Taylor & Francis, 2013.

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Danilo, De Rossi, and SpringerLink (Online service), eds. Wearable Monitoring Systems. Boston, MA: Springer Science+Business Media, LLC, 2011.

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Human activity recognition and gesture spotting with body-worn sensors. Konstanz: Hartung-Gorre Verlag, 2005.

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Ylli, Klevis, and Yiannos Manoli. Energy Harvesting for Wearable Sensor Systems. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4448-8.

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Jaafar, Mariatti, and Ye Zar Ni Htwe. Nanomaterials Based Printed Strain Sensor for Wearable Health Monitoring Applications. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5780-4.

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Book chapters on the topic "Wearable Sensors"

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Fang, Bin, Fuchun Sun, Huaping Liu, Chunfang Liu, and Di Guo. "Wearable Sensors." In Wearable Technology for Robotic Manipulation and Learning, 33–63. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5124-6_2.

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Foster, Robert, Tuba Yilmaz, Max Munoz, and Yang Hao. "Wearable Sensors." In Springer Series on Chemical Sensors and Biosensors, 95–125. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/5346_2012_28.

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Nag, Anindya, and Subhas Chandra Mukhopadhyay. "Wearable Electronics Sensors: Current Status and Future Opportunities." In Wearable Electronics Sensors, 1–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18191-2_1.

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Pimentel, Marco A. F., Peter H. Charlton, and David A. Clifton. "Probabilistic Estimation of Respiratory Rate from Wearable Sensors." In Wearable Electronics Sensors, 241–62. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18191-2_10.

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Occhiuzzi, C., C. Vallese, S. Amendola, S. Manzari, and G. Marrocco. "Ambient Intelligence System for the Remote Monitoring and Control of Sleep Quality." In Wearable Electronics Sensors, 263–82. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18191-2_11.

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Li, Guangyi, Tao Liu, and Yoshio Inoue. "Measurement of Human Gait Using a Wearable System with Force Sensors and Inertial Sensors." In Wearable Electronics Sensors, 283–98. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18191-2_12.

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Hanson, Valerie, and Kofi Odame. "Towards a Brain-Machine System for Auditory Scene Analysis." In Wearable Electronics Sensors, 299–320. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18191-2_13.

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Lin, Yingzi, and David Schmidt. "Wearable Sensing for Bio-feedback in Human Robot Interaction." In Wearable Electronics Sensors, 321–32. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18191-2_14.

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Xu, Wenyao, and Ming-Chun Huang. "TOTAL HEALTH: Toward Continuous Personal Monitoring." In Wearable Electronics Sensors, 37–56. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18191-2_2.

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Pirbhulal, Sandeep, Heye Zhang, Wanqing Wu, and YuanTing Zhang. "A Novel Biometric Algorithm to Body Sensor Networks." In Wearable Electronics Sensors, 57–79. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18191-2_3.

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Conference papers on the topic "Wearable Sensors"

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Gibbs, Peter, and H. Harry Asada. "Wearable Conductive Fiber Sensors for Continuous Joint Movement Monitoring." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59271.

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This paper describes a technique that uses conductive fibers as part of a wearable sensor for continuous monitoring of joint movements. Conductive fibers are incorporated into flexible, skin-tight fabrics that are comfortable and acceptable for long-term wear in everyday settings. Continuous monitoring of single or multi-axis joint movement is therefore possible, even when not in the presence of a therapist. A brief overview of the sensor design is presented, including functional requirements and important design parameters. Misalignment errors that may be created every time the subject takes off and puts on the wearable sensor are accounted for by incorporating an array of fiber sensors around the joint and analyzing each sensor’s sensitivity to joint movement during use. This eliminates any need for re-calibration after an initial calibration.
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Mihajlovic, Vojkan, Shrishail Patki, and Jiawei Xu. "Noninvasive wearable brain sensing." In 2017 IEEE SENSORS. IEEE, 2017. http://dx.doi.org/10.1109/icsens.2017.8234430.

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Yokus, Murat A., Talha Agcayazi, Matt Traenkle, Alper Bozkurt, and Michael A. Daniele. "Wearable Sweat Rate Sensors." In 2020 IEEE SENSORS. IEEE, 2020. http://dx.doi.org/10.1109/sensors47125.2020.9278818.

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Abshirini, Mohammad, Mohammad Charara, Mrinal C. Saha, M. Cengiz Altan, and Yingtao Liu. "Optimization of 3D Printed Elastomeric Nanocomposites for Flexible Strain Sensing Applications." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11467.

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Abstract Flexible and sensitive strain sensors can be utilized as wearable sensors and electronic devices in a wide range of applications, such as personal health monitoring, sports performance, and electronic skin. This paper presents the fabrication of a highly flexible and sensitive strain sensor by 3D printing an electrically conductive polydimethylsiloxane (PDMS)/multi-wall carbon nanotube (MWNT) nanocomposite on a PDMS substrate. To maximize the sensor’s gauge factor, the effects of MWNT concentration on the strain sensing function in nanocomposites are evaluated. Critical 3D printing and curing parameters, such as 3D printing nozzle diameter and nanocomposites curing temperature, are explored to achieve the highest piezoresistive response, showing that utilizing a smaller deposition nozzle size and higher curing temperature can result in a higher gauge factor. The optimized 3D printed nanocomposite sensor’s sensitivity is characterized under cyclic tensile loads at different maximum strains and loading rates. A linear piezoresistive response is observed up to 70% strain with an average gauge factor of 12, pointing to the sensor’s potential as a flexible strain sensor. In addition, the sensing function is almost independent of the applied load rate. The fabricated sensors are attached to a glove and used as a wearable sensor by detecting human finger and wrist motion. The results indicate that this 3D printed functional nanocomposite shows promise in a broad range of applications, including wearable and skin mounted sensors.
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Romero, Alberto Alonso, Koffi Amouzou, Andréane Richard-Denis, Jean-Marc Mac-Thiong, Yvan Petit, Jean-Marc Lina, and Bora Ung. "Development of a Wearable Optoelectronic Pressure Sensor Based on the Bending Loss of Plastic Optical Fiber and Polydimethylsiloxane." In Optical Sensors. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/sensors.2022.stu4c.3.

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We designed and demonstrated a PDMS-based flexible pressure sensor based on plastic optical fibers for measuring pressures up to 3820 mmHg with good repeatability. Its potential applications include wearable sensors for prevention of pressure injuries.
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Kukkapalli, Ruthvik, Nilanjan Banerjee, Ryan Robucci, and Yordan Kostov. "Micro-radar wearable respiration monitor." In 2016 IEEE SENSORS. IEEE, 2016. http://dx.doi.org/10.1109/icsens.2016.7808741.

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Yokus, Murat A., Cheyanne Hass, Talha Agcayazi, Alper Bozkurt, and Michael A. Daniele. "Towards a wearable perspiration sensor." In 2017 IEEE SENSORS. IEEE, 2017. http://dx.doi.org/10.1109/icsens.2017.8234296.

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Meguerdichian, Saro, Hyduke Noshadi, Foad Dabiri, and Miodrag Potkonjak. "Semantic multimodal compression for wearable sensing systems." In 2010 Ninth IEEE Sensors Conference (SENSORS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icsens.2010.5690381.

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Amft, Oliver. "A wearable earpad sensor for chewing monitoring." In 2010 Ninth IEEE Sensors Conference (SENSORS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icsens.2010.5690449.

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Francioso, L., C. De Pascali, I. Farella, C. Martucci, P. Creti, P. Siciliano, and A. Perrone. "Flexible thermoelectric generator for wearable biometric sensors." In 2010 Ninth IEEE Sensors Conference (SENSORS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icsens.2010.5690757.

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Reports on the topic "Wearable Sensors"

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Hegarty-Craver, Meghan, Hope Davis-Wilson, Pooja Gaur, Howard Walls, David Dausch, and Dorota Temple. Wearable Sensors for Service Members and First Responders: Considerations for Using Commercially Available Sensors in Continuous Monitoring. RTI Press, February 2024. http://dx.doi.org/10.3768/rtipress.2024.op.0090.2402.

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Wearable sensors (“wearables”) provide a mechanism to monitor the health of service members and first responders continuously and remotely. Several wearables are commercially available, with different configurations, sensors, algorithms, and forms of communication. Choosing the “best” wearable depends on the information you need to make decisions, how often you need this information, and the level of accuracy required. In this article, we review six use cases for wearables that are relevant to the military and first responders. We examine the metrics measured and the wearables used. We conclude with recommendations for requirements and wearable selection given the constraints imposed by various use cases.
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Claus, Ana, Borzooye Jafarizadeh, Azmal Huda Chowdhury, Neziah Pala, and Chunlei Wang. Testbed for Pressure Sensors. Florida International University, October 2021. http://dx.doi.org/10.25148/mmeurs.009771.

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Currently, several studies and experiments are being done to create a new generation of ultra-low-power wearable sensors. For instance, our group is currently working towards the development of a high-performance flexible pressure sensor. However, with the creation of new sensors, a need for a standard test method is necessary. Therefore, we opted to create a standardized testbed to evaluate the pressure applied to sensors. A pulse wave is generated when the heart pumps blood causing a change in the volume of the blood vessel. In order to eliminate the need of human subjects when testing pressure sensors, we utilized polymeric material, which mimics human flesh. The goal is to simulate human pulse by pumping air into a polymeric pocket which s deformed. The project is realized by stepper motor and controlled with an Arduino board. Furthermore, this device has the ability to simulate pulse wave form with different frequencies. This in turn allows us to simulate conditions such as bradycardia, tachycardia, systolic pressure, and diastolic pressure.
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Slattery, Patrick, Luis Eduardo Cofre Lizama, Jon Wheat, Paul Gastin, Ben Dascombe, and Kane Middleton. The Agreement Between Wearable Sensors and Force Plates for the Analysis of Stride Time. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317494.

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Imzilene, Ayoub, and Ayoub Lansi. "From Multi-Parameter to Single-Parameter: A Systematic review of Wearable Sensors sensitivity in Seizure Detection". INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2023. http://dx.doi.org/10.37766/inplasy2023.12.0011.

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Adlakha, Deepi, Jane Clarke, Perla Mansour, and Mark Tully. Walk-along and cycle-along: Assessing the benefits of the Connswater Community Greenway in Belfast, UK. Property Research Trust, 2021. http://dx.doi.org/10.52915/ghcj1777.

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Physical inactivity is a risk factor for numerous chronic diseases, and a mounting global health problem. It is likely that the outdoor physical environment, together with social environmental factors, has a tendency to either promote or discourage physical activity, not least in cities and other urban areas. However, the evidence base on this is sparse, making it hard to identify the best policy interventions to make, at the local or city level. This study seeks to assess the impact of one such intervention, the Connswater Community Greenway CCG), in Belfast, in Northern Ireland, UK. To do that it uses innovative methodologies, ‘Walk-along’ and ‘Cycle-along’ that involve wearable sensors and video footages, to improve our understanding of the impact of the CCG on local residents. The findings suggest that four characteristics of the CCG affect people’s activity and the benefits that the CCG created. These are physical factors, social factors, policy factors and individual factors. Each of these has many elements, with different impacts on different people using the greenway.
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Chon, Ki, and Yitzhak Mendelson. Wearable Wireless Sensor for Multi-Scale Physiological Monitoring. Fort Belvoir, VA: Defense Technical Information Center, October 2013. http://dx.doi.org/10.21236/ada590832.

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Bernhart, Severin, Eric Harbour, Ulf Jensen, and Thomas Finkenzeller. Wearable Chest Sensor for Running Stride and Respiration Detection. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317495.

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Boero, Riccardo, Peter Thomas Hraber, Kimberly Ann Kaufeld, Elisabeth Ann Moore, Ethan Romero-Severson, John Joseph Ambrosiano, John Leslie Whitton, and Benjamin Hayden Sims. Analysis of Multimodal Wearable Sensor Data to Characterize Social Groups and Influence in Organizations. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1570596.

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Jones, Michael, Sarah Ridge, Mia Caminita, Kirk E. Bassett, and Dustin Bruening. Automatic Classification of Take-off Type in Figure Skating Jumps Using a Wearable Sensor. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317496.

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Payne, John A. Sensing Disaster: The Use of Wearable Sensor Technology to Decrease Firefighter Line-of-Duty Deaths. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ad1009193.

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