Floating search methods in feature selection
Pattern Recognition Letters
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
TripleBeat: enhancing exercise performance with persuasion
Proceedings of the 10th international conference on Human computer interaction with mobile devices and services
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
FEEL: frequent EDA and event logging -- a mobile social interaction stress monitoring system
CHI '12 Extended Abstracts on Human Factors in Computing Systems
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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.