Using noninvasive wearable computers to recognize human emotions from physiological signals
EURASIP Journal on Applied Signal Processing
A Novel Heart Rate Detection Algorithm in Ballistocardiogram Based on Wavelet Transform
WKDD '09 Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining
An autonomic resource provisioning framework for mobile computing grids
Proceedings of the 9th international conference on Autonomic computing
Enabling Real-Time In-Situ Processing of Ubiquitous Mobile-Application Workflows
MASS '13 Proceedings of the 2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems
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Stress is one of the key factors that impacts the quality of our daily life. It is known that stress can propagate from one individual to others working in close proximity or towards a common goal, e.g., in a military operation or workforce, thus affecting productivity, efficiency, and the ability to make rational decisions. 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 is equally if not more important. In this paper, 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. G-causality has established itself as one of the promising non-invasive approaches in operational neuroscience to reveal the direction of influence between brain areas by analyzing temporal precedence. Extending G-causality analysis on real-time group data faces, however, communication and computation challenges, to address which a distributed mobile computational framework is employed and workflows defining how data and tasks are divided among the entities of the framework are designed.