Objective measures, sensors and computational techniques for stress recognition and classification

Published on Dec 1, 2012in Computer Methods and Programs in Biomedicine3.424
· DOI :10.1016/j.cmpb.2012.07.003
Nandita Sharma7
Estimated H-index: 7
(ANU: Australian National University),
Tom Gedeon33
Estimated H-index: 33
(ANU: Australian National University)
Stress is a major growing concern in our day and age adversely impacting both individuals and society. Stress research has a wide range of benefits from improving personal operations, learning, and increasing work productivity to benefiting society - making it an interesting and socially beneficial area of research. This survey reviews sensors that have been used to measure stress and investigates techniques for modelling stress. It discusses non-invasive and unobtrusive sensors for measuring computed stress, a term we coin in the paper. Sensors that do not impede everyday activities that could be used by those who would like to monitor stress levels on a regular basis (e.g. vehicle drivers, patients with illnesses linked to stress) is the focus of the discussion. Computational techniques have the capacity to determine optimal sensor fusion and automate data analysis for stress recognition and classification. Several computational techniques have been developed to model stress based on techniques such as Bayesian networks, artificial neural networks, and support vector machines, which this survey investigates. The survey concludes with a summary and provides possible directions for further computational stress research.
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