Affective computing
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Progress in Ambulatory Assessment
Progress in Ambulatory Assessment
The Mobile Sensing Platform: An Embedded Activity Recognition System
IEEE Pervasive Computing
Mobile Heart Health: Project Highlight
IEEE Pervasive Computing
Sensors Model Student Self Concept in the Classroom
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Out of the lab and into the fray: towards modeling emotion in everyday life
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
Detecting stress during real-world driving tasks using physiological sensors
IEEE Transactions on Intelligent Transportation Systems
Image and Vision Computing
MoodWings: a wearable biofeedback device for real-time stress intervention
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
Towards in situ affect detection in mobile devices: a multimodal approach
Proceedings of the 2013 Research in Adaptive and Convergent Systems
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
ACM SIGAPP Applied Computing Review
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One of the primary goals of affective computing is enabling computers to recognize human emotion. To do this we need accurately labeled affective data. This is challenging to obtain in real situations where affective events are not scripted and occur simultaneously with other activities and feelings. Affective labels also rely heavily on subject self-report for which can be problematic. This paper reports on methods for obtaining high quality emotion labels with reduced bias and variance and also shows that better training sets for machine learning algorithms can be created by combining multiple sources of evidence. During a 7 day, 13 participant field study we found that recognition accuracy for physiological activation improved from 63% to 79% with two sources of evidence and in an additional pilot study this improved to 100% accuracy for one subject over 10 days when context evidence was also included.