Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Recognizing the effects of voluntary facial activations using heart rate patterns
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
Facial Activation Control Effect (FACE)
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
IEEE Transactions on Information Technology in Biomedicine
Addressing the problems of data-centric physiology-affect relations modeling
Proceedings of the 15th international conference on Intelligent user interfaces
Researching emotion: challenges and solutions
Proceedings of the 2011 iConference
Information Processing and Management: an International Journal
Detecting leisure activities with dense motif discovery
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Understanding physiological responses to stressors during physical activity
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Hi-index | 0.00 |
Emotions powerfully influence our physiology, behavior, and experience. A comprehensive assessment of affective states in health and disease would include responses from each of these domains in real life. Since no single physiologic parameter can index emotional states unambiguously, a broad assessment of physiologic responses is desirable. We present a recently developed system, the LifeShirt, which allows reliable ambulatory monitoring of a wide variety of cardiovascular, respiratory, metabolic, motor-behavioral, and experiential responses. The system consists of a garment with embedded inductive plethysmography and other sensors for physiologic data recording and a handheld computer for input of experiential data via touch screen. Parameters are extracted offline using sophisticated analysis and display software. The device is currently used in clinical studies and to monitor effects of physical and emotional stress in naturalistic settings. Further development of signal processing and pattern recognition algorithms will enhance computerized identification of type and extent of physical and emotional activation.