Research Article
Pervasive and Unobtrusive Emotion Sensing for Human Mental Health
@INPROCEEDINGS{10.4108/icst.pervasivehealth.2013.252133, author={Rui Guo and Shuanjiang Li and Li He and Wei Gao and Hairong Qi and Gina Owens}, title={Pervasive and Unobtrusive Emotion Sensing for Human Mental Health}, proceedings={3rd International Workshop on Pervasive Computing Paradigms for Mental Health}, publisher={IEEE}, proceedings_a={MINDCARE}, year={2013}, month={5}, keywords={emotion sensing gsr data analysis emotion classification}, doi={10.4108/icst.pervasivehealth.2013.252133} }- Rui Guo
Shuanjiang Li
Li He
Wei Gao
Hairong Qi
Gina Owens
Year: 2013
Pervasive and Unobtrusive Emotion Sensing for Human Mental Health
MINDCARE
IEEE
DOI: 10.4108/icst.pervasivehealth.2013.252133
Abstract
In this paper, we present a pervasive and unobtrusive system for sensing human emotions, which are inferred based on the recording, processing, and analysis of the Galvanic Skin Response (GSR) signal from human bodies. Being different from traditional multimodal emotion sensing systems, our proposed system recognizes human emotions with the single modularity of GSR signal, which is captured by wearable sensing devices. A comprehensive set of features is extracted from GSR signal and fed into supervised classifiers for emotion identification. Our system has been evaluated by specific experiments to investigate the characteristics of human emotions in practice. The high accuracy of emotion classification highlights the great potential of this system in improving humans' mental health in the future.