Готові списки джерел за темами / Human activity monitoring

Добірка наукової літератури з теми "Human activity monitoring"

Автор: Grafiati

Опубліковано: 3 вересня 2022

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Статті в журналах з теми "Human activity monitoring":

Fiore, Loren, Duc Fehr, Robot Bodor, Andrew Drenner, Guruprasad Somasundaram, and Nikolaos Papanikolopoulos. "Multi-Camera Human Activity Monitoring." Journal of Intelligent and Robotic Systems 52, no. 1 (January 29, 2008): 5–43. http://dx.doi.org/10.1007/s10846-007-9201-6.

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Maenaka, Kazusuke. "Human Activity Monitoring with MEMS Technology." IEEJ Transactions on Sensors and Micromachines 134, no. 12 (2014): 372–77. http://dx.doi.org/10.1541/ieejsmas.134.372.

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Vettier, Benoit, and Catherine Garbay. "Abductive Agents for Human Activity Monitoring." International Journal on Artificial Intelligence Tools 23, no. 01 (February 2014): 1440002. http://dx.doi.org/10.1142/s0218213014400028.

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We propose in this paper a novel architecture for human activity monitoring, following conceptual, technical and experimental claims. From a conceptual viewpoint, we propose to approach the interpretation of sensor data as embedded into a multidimensional frame involving functional and non-functional requirements. Functional requirements involve considering the monitored person's specificities as well as the task to be performed. Non-functional requirements qualify the system activity. This frame of interpretation is continuously refined, to cope with evolving situations or expectations from the Observer. From a technical viewpoint, we propose to develop a multi-Agent architecture as a means for dependable, flexible monitoring. This paradigm allows to handle multiple, heterogeneous entities in a unified way. The Agents process incoming data with a dynamic population of hypotheses on several abstraction levels. This reasoning is abductive and fuzzy in nature. From the experimental viewpoint, we propose a dedicated evaluation approach to estimate the interpretative process unfolding. Functional and non-functional properties are presented to discuss the system's effectiveness, informativeness, sensitivity, efficiency and robustness, some of which are supported by qualitative, analytical discussions, others by quantitative measures.

MAENAKA, Kazusuke. "Human Activity Monitoring System by MEMS Devices." Journal of The Institute of Electrical Engineers of Japan 132, no. 3 (2012): 148–51. http://dx.doi.org/10.1541/ieejjournal.132.148.

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Nii, Manabu, Yoshihiro Kakiuchi, Kazunobu Takahama, Kazusuke Maenaka, Kohei Higuchi, and Takayuki Yumoto. "Human Activity Monitoring Using Fuzzified Neural Networks." Procedia Computer Science 22 (2013): 960–67. http://dx.doi.org/10.1016/j.procs.2013.09.180.

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Fonollosa, Jordi, Irene Rodriguez-Lujan, Abhijit V. Shevade, Margie L. Homer, Margaret A. Ryan, and Ramón Huerta. "Human activity monitoring using gas sensor arrays." Sensors and Actuators B: Chemical 199 (August 2014): 398–402. http://dx.doi.org/10.1016/j.snb.2014.03.102.

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Zhongna Zhou, Xi Chen, Yu-Chia Chung, Zhihai He, T. X. Han, and J. M. Keller. "Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring." IEEE Transactions on Circuits and Systems for Video Technology 18, no. 11 (November 2008): 1489–98. http://dx.doi.org/10.1109/tcsvt.2008.2005612.

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Maenaka, Kazusuke. "Miniaturized Human Activity Monitoring System with MEMS Technology." Journal of The Japan Institute of Electronics Packaging 23, no. 5 (August 1, 2020): 331–36. http://dx.doi.org/10.5104/jiep.23.331.

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Washino, Fumihiro, Yuki Matsumoto, Tomoya Tanaka, Koji Sonoda, Kensuke Kanda, Takayuki Fujita, and Kazusuke Maenaka. "Low Power ASIC for Monitoring Human Motion Activity." IEEJ Transactions on Sensors and Micromachines 135, no. 5 (2015): 178–83. http://dx.doi.org/10.1541/ieejsmas.135.178.

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Fujita, Takayuki, Jun Okada, Sayaka Okochi, Kohei Higuchi, and Kazusuke Maenaka. "Autonomous Environmental Sensing System for Human Activity Monitoring." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 3 (May 20, 2011): 383–88. http://dx.doi.org/10.20965/jaciii.2011.p0383.

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Continuous observation of human activities and circumstances are quite important for healthcare applications that collect a lot of data from the various MEMS (Micro Electromechanical Systems) sensors. This study demonstrates the multi-environmental sensing system for human applications that can measure the time-based three-axes acceleration (threeaxes shock), barometric pressure, temperature and relative humidity, simultaneously. The system has battery and large sized memory for autonomous sensing. The measured data are stored in a flash memory via an onboard microcontroller. The detailed configurations of the prototype device and some experimental results are investigated.

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Дисертації з теми "Human activity monitoring":

TOKALA, SAI SUJIT, and RANADEEP ROKALA. "HUMAN ACTIVITY MONITORING USING SMARTPHONE." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2566.

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The main aim of the project is to develop an algorithm which will classify the activity performed by a human who is carrying a smart phone. The day to day life made humans very busy at work and during daily activities, mostly elderly people who are at home have an important need to monitor their activity by others when they are alone, if they are inactive for a long time without movement, or in some situations like if they have fallen down, became unconscious for sometime or seized with a cardiac arrest etc… will help the observer to know the state of activity of person being monitored. In this project we develop an algorithm to know the activity of a person using accelerometer available in Smartphone. We have extracted the Smartphone accelerometer data using an application called accelerometer data logger version 1.0 available in Smartphone market and have processed the data in Matlab for classifying the different activities of human being into static and dynamic activity, if the activity is dynamic then further classification into walking or running is performed with the algorithm. We implemented smoothening filters for data analysis and statistical techniques like standard deviation, mean and signal magnitude analysis for activity classification. This classification algorithm will let us know the type of activity either static or dynamic and then classify the position of the user, such as walking, running or ideal, which can provide useful information for the observer who is monitoring the activities of wearer, and which will help the wearer for his daily living. To bring out the extensive use of algorithm and to provide valuable feedback for wearer regarding his activities, energy spent by user during the activities was calculated at a given time using regression methods and was implemented in the algorithm. The developed model was able to estimate the energy spent by the user, the observations recorded were almost similar to the treadmill data which is taken as a standard for our model and the mean error is not more than ±2 for 30 observations. The final results when compared with the standard model was proved to be 93 % accurate on average of 30 subjects data which is used for verifying the algorithm developed. With these set of results we have come to a conclusion that algorithm can be easily implemented in a real time Smartphone application with low false predictions and can be implemented with low computational cost and fast real-time response. In future our classification algorithm can also be used in military applications where one can know what the soldier is doing without actually seeing him and additionally it can be proved as a support system in athlete’s health monitoring and training.
I denna modell har vi utvecklat en algoritm för aktivitetsklassificeringoch energiförbrukning uppskattning , vilket hjälper oss i övervakningen daglig mänsklig aktivitet med större noggrannhet . Resultaten valideras med standard energiförbrukning teknik och aktivitetsklassificeringsvideoobservationer. Vi vill att denna modell ska integreras i smarta mobiltelefoner för att ge slutanvändaren en vänlig atmosfär utan att lägga några komplicerade funktioner för hantering av utrustningen . Denna modell är mycket användbart i klinisk uppföljning av patienterna , kommer det att hjälpa oss att övervaka gamla , sjuka och utvecklingsstörda personens aktivitetsidentifiering och hjälper oss i nära övervakning av patienterna men fysiskt att vara borta från dem . Våra bärbara MEMS baserade treaxlig accelerometer system baserat smartphone kompatibel algoritm tillsammans med andra fysiologiska övervakningsparametrarkommer att ge korrekt övervakning rörelse och energiförbrukning uppskattning för klinisk analys . Denna modell är användbar för analys och övervakning av grupp -och enskilda individer , vilket kommer att leda till att spåra deras rörelser och en framgångsrik räddningsaktion för att rädda dem från dödliga sjukdomar och förebygga risker när de är skadade . Framtida arbete kommer att vara kontinuerlig övervakning av ämnen enskild aktivitet tillsammans med gruppaktivitet . Identifiera hållning övergång av olika aktiviteter i en kort tid som att springa till sittande , sittande till stående , står att krypa etc.
0091-7660885577

Wusk, Grace Caroline. "Psychophysiological Monitoring of Crew State for Extravehicular Activity." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103386.

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A spacewalk, or extravehicular activity (EVA), is one of the most mission critical and physically and cognitively challenging tasks that crewmembers complete. With next-generation missions to the Moon and Mars, exploration EVA will challenge crewmembers in partial gravity environments with increased frequency, duration, and autonomy of operations. Given the distance from Earth, associated communication delays, and durations of exploration missions, there is a monumental shift in responsibility and authority taking place in spaceflight; moving from Earth-dependent to crew self-reliant. For the safety, efficacy, and efficiency of future surface EVAs, there is a need to better understand crew health and performance. With this knowledge, technology and operations can be designed to better support future crew autonomy. The focus of this dissertation is to develop and evaluate a psychophysiological monitoring tool to classify cognitive workload during an operationally relevant EVA task. This was completed by compiling a sensor suite of commercial wearable devices to record physiological signals in two human research studies, one at Virginia Tech and one at NASA Johnson Space Center. The approach employs supervised machine learning to recognize patterns in psychophysiological features across different psychological states. This relies on the ability to simulate, or induce, cognitive workload in order to label data for training the model. A virtual reality (VR) Translation Task was developed to control and quantify cognitive demands during an immersive, ambulatory EVA scenario. Participants walked on a passive treadmill while wearing a VR headset to move along a virtual lunar surface. They walked with constraints on time and resources, while simultaneously identifying and recalling waypoints in the scene. Psychophysiological features were extracted and labeled according to the task demands, i.e. high or low cognitive workload, for the novel Translation Task, as well as for the benchmark Multi-Attribute Task Battery (MATB). Predictive models were created using the K Nearest Neighbor (KNN) algorithm. The contributions of this dissertation span the simulation, characterization, and modeling of cognitive state. Ultimately, this work tests the limits of extending laboratory psychophysiological monitoring to more realistic environments using wearable devices, and of generalizing predictive models across participants, times, and tasks. This work paves the way for future field studies and real-time implementation to close the loop between human and automation.
Doctor of Philosophy
A spacewalk is one of the most important and physically and mentally challenging tasks that astronauts complete. With next-generation missions to the Moon and Mars, exploration spacewalks will challenge astronauts in reduced-weight environments (1/6 and 1/3 Earth's gravity) with longer, more frequent spacewalks and with less help from mission control. To keep astronauts safe while exploring there is a need to better understand astronaut health and performance (physical and mental) during spacewalks. With knowledge of how astronauts will respond to high workload and stressful events, we can plan missions and design tools that can best assist them during spacewalks on the Moon and Mars when help from Earth mission control is limited. Traditional tools of quantifying mental state are not suitable for real-time assessment during spacewalks. Current methods, including subjective surveys and performance-based computer tests, require time and attention to complete and cannot assess real-time operations. The focus of this dissertation is to create a psychophysiological monitoring tool to measure mental workload during a virtual reality (VR) spacewalk. Psychophysiological monitoring uses physiological measures, like heart rate and breathing rate, to predict psychological state, like high workload or stress. Physiological signals were recorded using commercial wearable devices in two human research studies, one at Virginia Tech and one at NASA Johnson Space Center. With machine learning, computer models can be trained to recognize patterns in physiological measures for different psychological states. Once a model is trained, it can be tested on new data to predict mental workload. To train and test the models, participants in the studies completed high and low workload versions of the VR task. The VR task was specifically designed for this study to simulate and measure performance during a mentally-challenging spacewalk scenario. The participants walked at their own pace on a treadmill while wearing a VR headset to move along a virtual lunar surface, while balancing their time and resources. They were also responsible for identifying and recalling flags along their virtual path. Ultimately, this work tests the limits of extending laboratory psychophysiological monitoring to more realistic environments using wearable devices, and of generalizing predictive models across participants, times, and tasks. This work paves the way for future field studies and real-time implementation to close the loop between human and automation.

Rajkumar, Reuben Sajith. "Monitoring Human Activity Patterns in Linnaean Botanical Gardens using Machine Learning." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-449230.

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Urbanisation in this fast-paced world although possessing some lucrative advantages causes some serious problems to its inhabitants. Green spaces are important in highly urbanised societies for adequate restoration of mental health and physical well being. This study focuses on understanding people’s behaviour in green spaces. To enable this, this study was designed with a video of volunteers in a greenspace. In order to automate the data collection required to observe the participants and study their behavioural patterns, computer science aided interventions and machine learning algorithms were employed. YOLOv4 enabled the detection of objects using a regression-based approach to accurately determine the position of the bounding boxes. Using the bounding box coordinates, experiments were conducted with several use cases like hotspot detection and crowd detection. Further using transfer learning, attempts were made to recognize the actions of humans in the videos. The experiments were evaluated using the mean Average Precision technique and achieved good results for the use cases mentioned above. With implications in hotspot detection and crowd detection, the outcome of the study can contribute towards a better and efficient object detection and action recognition.

Gillman, Mark Daniel. "Interpreting human activity from electrical consumption data through non-intrusive load monitoring." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90136.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
50
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 155-160).
Non-intrusive load monitoring (NILM) has three distinct advantages over today's smart meters. First, it offers accountability. Few people know where their kWh's are going. Second, it is a maintenance tool. Signs of wear are detectable through their electrical signal. Third, it provides awareness of human activity within a network. Each device has an electrical fingerprint, and specific devices imply associated human actions. From voltage and current measurements at a single point on the network, non-intrusive load monitoring (NILM) disaggregates appliance-level information. This information is available remotely in bandwidth-constrained environments. Four real-world field tests at military micro grids and commercial buildings demonstrate the utility of the NILM in reducing electrical demand, enabling condition-based maintenance, and inferring human activity from electrical activity.
by Mark Daniel Gillman.
S.M.

Tun, Min Han. "Virtual image sensors to track human activity in a smart house." Curtin University of Technology, School of Computing, 2007. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=17557.

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With the advancement of computer technology, demand for more accurate and intelligent monitoring systems has also risen. The use of computer vision and video analysis range from industrial inspection to surveillance. Object detection and segmentation are the first and fundamental task in the analysis of dynamic scenes. Traditionally, this detection and segmentation are typically done through temporal differencing or statistical modelling methods. One of the most widely used background modeling and segmentation algorithms is the Mixture of Gaussians method developed by Stauffer and Grimson (1999). During the past decade many such algorithms have been developed ranging from parametric to non-parametric algorithms. Many of them utilise pixel intensities to model the background, but some use texture properties such as Local Binary Patterns. These algorithms function quite well under normal environmental conditions and each has its own set of advantages and short comings. However, there are two drawbacks in common. The first is that of the stationary object problem; when moving objects become stationary, they get merged into the background. The second problem is that of light changes; when rapid illumination changes occur in the environment, these background modelling algorithms produce large areas of false positives.
These algorithms are capable of adapting to the change, however, the quality of the segmentation is very poor during the adaptation phase. In this thesis, a framework to suppress these false positives is introduced. Image properties such as edges and textures are utilised to reduce the amount of false positives during adaptation phase. The framework is built on the idea of sequential pattern recognition. In any background modelling algorithm, the importance of multiple image features as well as different spatial scales cannot be overlooked. Failure to focus attention on these two factors will result in difficulty to detect and reduce false alarms caused by rapid light change and other conditions. The use of edge features in false alarm suppression is also explored. Edges are somewhat more resistant to environmental changes in video scenes. The assumption here is that regardless of environmental changes, such as that of illumination change, the edges of the objects should remain the same. The edge based approach is tested on several videos containing rapid light changes and shows promising results. Texture is then used to analyse video images and remove false alarm regions. Texture gradient approach and Laws Texture Energy Measures are used to find and remove false positives. It is found that Laws Texture Energy Measure performs better than the gradient approach. The results of using edges, texture and different combination of the two in false positive suppression are also presented in this work. This false positive suppression framework is applied to a smart house senario that uses cameras to model ”virtual sensors” to detect interactions of occupants with devices. Results show the accuracy of virtual sensors compared with the ground truth is improved.

Mar, Therese Frances. "The effects of physical activity and gender on the toxicokinetics of toluene in human volunteers /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/8441.

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Turner, Tyler Norman. "Effects of Human Land Use on the Activity, Diversity, and Distribution of Native Bats." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1522839181353869.

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Westeyn, Tracy Lee. "Child's play: activity recognition for monitoring children's developmental progress with augmented toys." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34697.

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The way in which infants play with objects can be indicative of their developmental progress and may serve as an early indicator for developmental delays. However, the observation of children interacting with toys for the purpose of quantitative analysis can be a difficult task. To better quantify how play may serve as an early indicator, researchers have conducted retrospective studies examining the differences in object play behaviors among infants. However, such studies require that researchers repeatedly inspect videos of play often at speeds much slower than real-time to indicate points of interest. The research presented in this dissertation examines whether a combination of sensors embedded within toys and automatic pattern recognition of object play behaviors can help expedite this process. For my dissertation, I developed the Child'sPlay system which uses augmented toys and statistical models to automatically provide quantitative measures of object play interactions, as well as, provide the PlayView interface to view annotated play data for later analysis. In this dissertation, I examine the hypothesis that sensors embedded in objects can provide sufficient data for automatic recognition of certain exploratory, relational, and functional object play behaviors in semi-naturalistic environments and that a continuum of recognition accuracy exists which allows automatic indexing to be useful for retrospective review. I designed several augmented toys and used them to collect object play data from more than fifty play sessions. I conducted pattern recognition experiments over this data to produce statistical models that automatically classify children's object play behaviors. In addition, I conducted a user study with twenty participants to determine if annotations automatically generated from these models help improve performance in retrospective review tasks. My results indicate that these statistical models increase user performance and decrease perceived effort when combined with the PlayView interface during retrospective review. The presence of high quality annotations are preferred by users and promotes an increase in the effective retrieval rates of object play behaviors.

Mokhlespour, Esfahani Mohammad Iman. "Development and Assessment of Smart Textile Systems for Human Activity Classification." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/97249.

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Wearable sensors and systems have become increasingly popular for diverse applications. An emerging technology for physical activity assessment is Smart Textile Systems (STSs), comprised of sensitive/actuating fiber, yarn, or fabric that can sense an external stimulus. All required components of an STS (sensors, electronics, energy supply, etc.) can be conveniently embedded into a garment, providing a fully textile-based system. Thus, STSs have clear potential utility for measuring health-relevant aspects of human activity, and to do so passively and continuously in diverse environments. For these reasons, STSs have received increasing interest in recent studies. Despite this, however, limited evidence exists to support the implementation of STSs during diverse applications. Our long-term goal was to assess the feasibility and accuracy of using an STS to monitor human activities. Our immediate objective was to investigate the accuracy of an STS in three representative applications with respect to occupational scenarios, healthcare, and activities of daily living. A particular STS was examined, consisting of a smart socks (SSs), using textile pressure sensors, and smart undershirt (SUS), using textile strain sensors. We also explored the relative merits of these two approaches, separately and in combination. Thus, five studies were completed to design and evaluate the usability of the smart undershirt, and investigate the accuracy of implementing an STS in the noted applications. Input from the SUS led to planar angle estimations with errors on the order of 1.3 and 9.4 degrees for the low-back and shoulder, respectively. Overall, individuals preferred wearing a smart textile system over an IMU system and indicated the former as superior in several aspects of usability. In particular, the short-sleeved T-shirt was the most preferred garments for an STS. Results also indicated that the smart shirt and smart socks, both individually and in combination, could detect occupational tasks, abnormal and normal gaits, and activities of daily living with greater than 97% accuracy. Based on our findings, we hope to facilitate future work that more effectively quantifies sedentary periods that may be deleterious to human health, as well as detect activity types that may be help or hinder health and fitness. Such information may be of use to individuals and workers, healthcare providers, and ergonomists. More specifically, further analyses from this investigation could provide strategies for: (a) modifying a sedentary lifestyle or work scenario to a more active one, and (b) helping to more accurately identify occupational injury risk factors associated with human movement.
PHD

Tsitsoulis, Athanasios. "A Methodology for Extracting Human Bodies from Still Images." Wright State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1389793781.

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Книги з теми "Human activity monitoring":

Section, Minnesota Dept of Human Services Community Services Evaluation. Community Services Evaluation Section social services monitoring activity report. [St. Paul: Minnesota Dept. of Human Services, 1990.

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Marsland, Stephen, and Hans Werner Güsgen. Human behavior recognition technologies: Intelligent applications for monitoring and security. Hershey, PA: Information Science Reference, 2013.

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Hodakov, Viktor. Natural environment and human activity. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1194879.

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The monograph describes the influence of the natural environment and its natural and climatic conditions on human life and socio-economic systems, which are considered as regions, territories of Eastern Europe. The natural and climatic factors (PCFs) characterizing the natural environment of Eastern Europe (Russia and Ukraine) and Western (England and France) are considered. Eastern Europe is in the zone of negative PCFs, close to critical. The influence of the PCF on the vital activity of the state and man is systematically described: mentality, systemic thinking, human health, ensuring the safety of life, sustainability of development, agricultural production, housing and communal services, construction, industry, information security, parrying of the PCF, the influence of the PCF on the development of science and education. Climate change trends at the global and regional levels are also described. Estimates of the impact of the PCF on the economy of the state and regions, recommendations on the adaptation of the economy to the PCF, the relationship of information security and information about the PCF, information technologies for assessing the sustainability of development and investment attractiveness of territories, conceptual foundations of state anti-crisis management of socio-economic systems are presented. It is intended for researchers, teachers, postgraduates, students specializing in the field of life safety, computer ecological and economic monitoring. It can be used to educate society in the field of the natural environment and its natural and climatic conditions.

M, Smith Heather. Insincere commitments: Human rights treaties, abusive states, and citizen activism. Washington D.C: Georgetown University Press, 2012.

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Seidman, G. Beyond the boycott: Labor rights, human rights, and transnational activism. New York: Russell Sage Foundation, 2007.

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Seidman, G. Beyond the boycott: Labor rights, human rights, and transnational activism. New York, NY: Russell Sage Foundation, 2006.

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Grabe, Shelly, ed. Women's Human Rights. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190614614.001.0001.

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Women’s Human Rights: A Social Psychological Perspective on Resistance, Liberation, and Justice contributes to the discussion of why women’s human rights warrant increased focus in the context of globalization. It considers how psychology can provide the links between transnational feminism and the discourse on women’s human rights and neoliberalism by using activist scholarship and empirical findings based on women’s grassroots resistance. The book takes a radically different approach to women’s human rights than disciplines such as law, for example, by developing new ideas regarding how psychology can be relevant in the study or actualization of women’s human rights and by making clear how activist-scholarship can make a unique contribution to the defense of women’s rights. This radical departure from using a legal framework, or examples that have been sensationalized throughout academia and advocacy (e.g., genital cutting), provides a route for better understanding how the mechanisms of violation operate. Thus, it has the potential to offer alternatives for intervention that extend beyond changing laws or monitoring international human rights treaties. The perspectives offered by the authors are largely informed by feminist liberation psychology, women of color, and critical race and queer theories in an attempt to demonstrate how research in psychology can shed light on the diverse experiences of women resisting human rights violations and to suggest means by which psychological processes can effectively challenge the broader structures of power that exacerbate the violation of women’s rights.

Sutter, Raoul, Peter W. Kaplan, and Donald L. Schomer. Historical Aspects of Electroencephalography. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0001.

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Electroencephalography (EEG), a dynamic real-time recording of electrical neocortical brain activity, began in the 1600s with the discovery of electrical phenomena and the concept of an “action current.” The galvanometer was introduced in the 1800s and the first bioelectrical observations of human brain signals were made in the 1900s. Certain EEG patterns were associated with brain disorders, increasing the clinical and scientific use of EEG. In the 1980s, technical advances allowed EEGs to be digitized and linked with videotape recording. In the 1990s, digital data storage increased and computer networking enabled remote real-time EEG reading, which made possible continuous EEG (cEEG) monitoring. Manual cEEG analysis became increasingly labor-intensive, calling for methods to assist this process. In the 2000s, complex algorithms enabling quantitative EEG analyses were introduced, with a new focus on shared activity between rhythms, including phase and magnitude synchrony. The automation of spectral analysis enabled studies of spectral content.

Gathii, James Thuo. The East African Court of Justice. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198795582.003.0003.

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This chapter discusses how human rights advocates and business actors resort to the East African Court of Justice (EACJ). The EACJ has intermediate authority at a thin-elite level in human rights cases because urban-based, human rights nongovernmental organizations, pro-democracy activists, and governmental officials recognize the legally binding nature of the EACJ’s human rights cases and give effect to its rulings. Human rights advocates have litigated cases in the EACJ, even though the EACJ does not have explicit jurisdiction to decide human rights cases. Business actors in general, and the East African Business Council (EABC) in particular, have eschewed litigating before the EACJ. Over the last decade the EABC has pursued an administrative strategy embodied in the NTB Monitoring Mechanism for monitoring, reporting, and removing nontariff barriers (NTBs). Also discussed is the first case filed by a business actor in 2017 relating to an alleged violation of the EAC’s trade rules.

Seidman, Gay W. Beyond the Boycott: Labor Rights, Human Rights, and Transnational Activism (American Sociological Association's Rose Series in Sociology). Russell Sage Foundation Publications, 2007.

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Частини книг з теми "Human activity monitoring":

Billah, Mohammad Saad, Md Atiqur Rahman Ahad, and Upal Mahbub. "Signal Processing for Contactless Monitoring." In Contactless Human Activity Analysis, 113–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68590-4_4.

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Mahbub, Upal, Tauhidur Rahman, and Md Atiqur Rahman Ahad. "Contactless Human Monitoring: Challenges and Future Direction." In Contactless Human Activity Analysis, 335–64. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68590-4_12.

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Zhang, Daqing, Kai Niu, Jie Xiong, Fusang Zhang, and Shengjie Li. "Location Independent Vital Sign Monitoring and Gesture Recognition Using Wi-Fi." In Contactless Human Activity Analysis, 185–202. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68590-4_7.

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Kröse, Ben, Tim van Oosterhout, and Tim van Kasteren. "Activity Monitoring Systems in Health Care." In Computer Analysis of Human Behavior, 325–46. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-994-9_12.

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Leon, Javier Lamar, Raúl Alonso, Edel Garcia Reyes, and Rocio Gonzalez Diaz. "Topological Features for Monitoring Human Activities at Distance." In Activity Monitoring by Multiple Distributed Sensing, 40–51. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13323-2_4.

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Vugic, Domagoj, Åsa Ehlén, and Aura Carreira. "Monitoring Homologous Recombination Activity in Human Cells." In Homologous Recombination, 115–26. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0644-5_9.

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Sang, Vu Ngoc Thanh, Nguyen Duc Thang, Vo Van Toi, Nguyen Duc Hoang, and Truong Quang Dang Khoa. "Human Activity Recognition and Monitoring Using Smartphones." In IFMBE Proceedings, 481–85. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11776-8_119.

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Tani, T., K. Kida, H. Yamamoto, and J. Kimura. "Reflexes Evoked in Various Human Muscles During Voluntary Activity." In Spinal Cord Monitoring and Electrodiagnosis, 226–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-75744-0_30.

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Sidorov, Konstantin V., Natalya I. Bodrina, and Natalya N. Filatova. "Monitoring Human Cognitive Activity Through Biomedical Signal Analysis." In Advances in Neural Computation, Machine Learning, and Cognitive Research IV, 309–15. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60577-3_37.

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Koul, Shiban Kishen, and Richa Bharadwaj. "Wearable Technology for Human Activity Monitoring and Recognition." In Lecture Notes in Electrical Engineering, 191–218. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3973-9_7.

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Тези доповідей конференцій з теми "Human activity monitoring":

Rafiq, Azhar, Xiaoming Zhao, Cosmin Boanca, Esther Hughes, and Ronald Merrell. "Human Systems Monitoring during Extravehicular Activity." In International Conference On Environmental Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2006. http://dx.doi.org/10.4271/2006-01-2206.

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Gabriel, Iana Vasile, and Petre Anghelescu. "Vibration monitoring system for human activity detection." In 2015 7th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2015. http://dx.doi.org/10.1109/ecai.2015.7301184.

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Uddin, Mostafa, Ahmed Salem, Ilho Nam, and Tamer Nadeem. "Wearable Sensing Framework for Human Activity Monitoring." In the 2015 workshop. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2753509.2753513.

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Wijayasekara, D., and M. Manic. "Human machine interaction via brain activity monitoring." In 2013 6th International Conference on Human System Interactions (HSI). IEEE, 2013. http://dx.doi.org/10.1109/hsi.2013.6577809.

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Sriwan, Jakkrit, and Wannarat Suntiamorntut. "Human activity monitoring system based on WSNs." In 2015 12th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2015. http://dx.doi.org/10.1109/jcsse.2015.7219804.

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Mitsou, Alexandros, Evaggelos Spyrou, and Theodoros Giannakopoulos. "Multimodal Workplace Monitoring for Human Activity Recognition." In PCI 2021: 25th Pan-Hellenic Conference on Informatics. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3503823.3503862.

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Fujita, Takayuki, Kentaro Masaki, Fumiaki Suzuki, and Kazusuke Maenaka. "Wireless MEMS Sensing System for Human Activity Monitoring." In 2007 IEEE/ICME International Conference on Complex Medical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/iccme.2007.4381768.

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Sebestyen, Gheorghe, Ionut Stoica, and Anca Hangan. "Human activity recognition and monitoring for elderly people." In 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2016. http://dx.doi.org/10.1109/iccp.2016.7737171.

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Uslu, Gamze, Ozgur Altun, and Sebnem Baydere. "A Bayesian approach for indoor human activity monitoring." In 2011 11th International Conference on Hybrid Intelligent Systems (HIS 2011). IEEE, 2011. http://dx.doi.org/10.1109/his.2011.6122126.

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Sharma, Annapurna, Young-Dong Lee, and Wan-Young Chung. "High Accuracy Human Activity Monitoring Using Neural Network." In 2008 Third International Conference on Convergence and Hybrid Information Technology (ICCIT). IEEE, 2008. http://dx.doi.org/10.1109/iccit.2008.394.

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Звіти організацій з теми "Human activity monitoring":

Willis, C., F. Jorgensen, S. A. Cawthraw, H. Aird, S. Lai, M. Chattaway, I. Lock, E. Quill, and G. Raykova. A survey of Salmonella, Escherichia coli (E. coli) and antimicrobial resistance in frozen, part-cooked, breaded or battered poultry products on retail sale in the United Kingdom. Food Standards Agency, May 2022. http://dx.doi.org/10.46756/sci.fsa.xvu389.

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Frozen, breaded, ready-to-cook chicken products have been implicated in outbreaks of salmonellosis. Some of these outbreaks can be large. For example, one outbreak of Salmonella Enteritidis involved 193 people in nine countries between 2018 and 2020, of which 122 cases were in the UK. These ready-to-cook products have a browned, cooked external appearance, which may be perceived as ready-to-eat, leading to mishandling or undercooking by consumers. Continuing concerns about these products led FSA to initiate a short-term (four month), cross-sectional surveillance study undertaken in 2021 to determine the prevalence of Salmonella spp., Escherichia coli and antimicrobial resistance (AMR) in frozen, breaded or battered chicken products on retail sale in the UK. This study sought to obtain data on AMR levels in Salmonella and E. coli in these products, in line with a number of other FSA instigated studies of the incidence and nature of AMR in the UK food chain, for example, the systematic review (2016). Between the beginning of April and the end of July 2021, 310 samples of frozen, breaded or battered chicken products containing either raw or partly cooked chicken, were collected using representative sampling of retailers in England, Wales, Scotland and Northern Ireland based on market share data. Samples included domestically produced and imported chicken products and were tested for E. coli (including extended-spectrum beta-lactamase (ESBL)-producing, colistin-resistant and carbapenem-resistant E. coli) and Salmonella spp. One isolate of each bacterial type from each contaminated sample was randomly selected for additional AMR testing to determine the minimum inhibitory concentration (MIC) for a range of antimicrobials. More detailed analysis based on Whole Genome Sequencing (WGS) data was used to further characterise Salmonella spp. isolates and allow the identification of potential links with human isolates. Salmonella spp. were detected in 5 (1.6%) of the 310 samples and identified as Salmonella Infantis (in three samples) and S. Java (in two samples). One of the S. Infantis isolates fell into the same genetic cluster as S. Infantis isolates from three recent human cases of infection; the second fell into another cluster containing two recent cases of infection. Countries of origin recorded on the packaging of the five Salmonella contaminated samples were Hungary (n=1), Ireland (n=2) and the UK (n=2). One S. Infantis isolate was multi-drug resistant (i.e. resistant to three different classes of antimicrobials), while the other Salmonella isolates were each resistant to at least one of the classes of antimicrobials tested. E. coli was detected in 113 samples (36.4%), with counts ranging from <3 to >1100 MPN (Most Probable Number)/g. Almost half of the E. coli isolates (44.5%) were susceptible to all antimicrobials tested. Multi-drug resistance was detected in 20.0% of E. coli isolates. E. coli isolates demonstrating the ESBL (but not AmpC) phenotype were detected in 15 of the 310 samples (4.8%) and the AmpC phenotype alone was detected in two of the 310 samples (0.6%) of chicken samples. Polymerase Chain Reaction (PCR) testing showed that five of the 15 (33.3%) ESBL-producing E. coli carried blaCTX-M genes (CTX-M-1, CTX-M-55 or CTX-M-15), which confer resistance to third generation cephalosporin antimicrobials. One E. coli isolate demonstrated resistance to colistin and was found to possess the mcr-1 gene. The five Salmonella-positive samples recovered from this study, and 20 similar Salmonella-positive samples from a previous UKHSA (2020/2021) study (which had been stored frozen), were subjected to the cooking procedures described on the sample product packaging for fan assisted ovens. No Salmonella were detected in any of these 25 samples after cooking. The current survey provides evidence of the presence of Salmonella in frozen, breaded and battered chicken products in the UK food chain, although at a considerably lower incidence than reported in an earlier (2020/2021) study carried out by PHE/UKHSA as part of an outbreak investigation where Salmonella prevalence was found to be 8.8%. The current survey also provides data on the prevalence of specified AMR bacteria found in the tested chicken products on retail sale in the UK. It will contribute to monitoring trends in AMR prevalence over time within the UK, support comparisons with data from other countries, and provide a baseline against which to monitor the impact of future interventions. While AMR activity was observed in some of the E. coli and Salmonella spp. examined in this study, the risk of acquiring AMR bacteria from consumption of these processed chicken products is low if the products are cooked thoroughly and handled hygienically.