Wearable and Implantable Body Sensor Networks, International Workshop on (2009)
June 3, 2009 to June 5, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/BSN.2009.13
This article describes an approach to detecting mental stress using unobtrusive wearable sensors. The approach relies on estimating the state of the autonomic nervous system from an analysis of heart rate variability. Namely, we use a non-linear system identification technique known as principal dynamic modes (PDM) to predict the activation level of the two autonomic branches: sympathetic (i.e. stress-inducing) and parasympathetic (i.e. relaxation-related). We validate the method on a discrimination problem with two psychophysiological conditions, one associated with mental tasks and one induced by relaxation exercises. Our results indicate that PDM features are more stable and less subject-dependent than spectral features, though the latter provide higher classification performance within subjects. When PDM and spectral features are combined, our system discriminates stressful events with a success rate of 83% within subjects (69% between subjects).
autonomic nervous system, heart rate variability, mental stress, wearable sensors, principal dynamic modes
R. Gutierrez-Osuna and J. Choi, "Using Heart Rate Monitors to Detect Mental Stress," 2009 Sixth International Workshop on Wearable & Implantable Body Sensor Networks Conference (BSN 2009)(BSN), Berkeley, CA, 2009, pp. 219-223.