A Distributed Computing Framework for Real-Time Detection of Stress and of Its Propagation in a Team

Stress is one of the key factor that impacts the quality of our daily life: From the productivity and efficiency in the production processes to the ability of (civilian and military) individuals in making rational decisions. Also, stress can propagate from one individual to other working in a close...

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Vydané v:IEEE journal of biomedical and health informatics Ročník 20; číslo 6; s. 1502 - 1512
Hlavní autori: Pandey, Parul, Eun Kyung Lee, Pompili, Dario
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.11.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Biomedical monitoring
Causality
Computer Simulation
Distributed mobile computing
Distributed processing
Electrocardiography
Energy expenditure
experiments
Feature extraction
Granger causality
Humans
Military
Mobile handsets
Models, Statistical
Monitoring
Monitoring, Physiologic - methods
Propagation
Real time
real-time stress detection
Real-time systems
Sensors
Signal Processing, Computer-Assisted
Stress
Stress (physiology)
Stress, Physiological - physiology
Stress, Psychological - diagnosis
ISSN:2168-2194, 2168-2208
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Shrnutí:Stress is one of the key factor that impacts the quality of our daily life: From the productivity and efficiency in the production processes to the ability of (civilian and military) individuals in making rational decisions. Also, stress can propagate from one individual to other working in a close proximity or toward a common goal, e.g., in a military operation or workforce. Real-time assessment of the stress of individuals alone is, however, not sufficient, as understanding its source and direction in which it propagates in a group of people is equally-if not more-important. A continuous near real-timein situ personal stress monitoring system to quantify level of stress of individuals and its direction of propagation in a team is envisioned. However, stress monitoring of an individual via his/her mobile device may not always be possible for extended periods of time due to limited battery capacity of these devices. To overcome this challenge a novel distributed mobile computing framework is proposed to organize the resources in the vicinity and form a mobile device cloud that enables offloading of computation tasks in stress detection algorithm from resource constrained devices (low residual battery, limited CPU cycles) to resource rich devices. Our framework also supports computing parallelization and workflows, defining how the data and tasks divided/assigned among the entities of the framework are designed. The direction of propagation and magnitude of influence of stress in a group of individuals are studied by applying real-time, in situ analysis of Granger Causality. Tangible benefits (in terms of energy expenditure and execution time) of the proposed framework in comparison to a centralized framework are presented via thorough simulations and real experiments.
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ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2015.2477342