A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Linking between Text and Photos Using Common Sense Reasoning
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Conversational scene analysis
MIThril 2003: Applications and Architecture
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Sensing and modeling human networks
Sensing and modeling human networks
A sensor network for social dynamics
Proceedings of the 5th international conference on Information processing in sensor networks
Human computing for interactive digital media
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Engineering contextual knowledge for autonomic pervasive services
Information and Software Technology
Creating dynamic groups using context-awareness
Proceedings of the 6th international conference on Mobile and ubiquitous multimedia
Knowledge networks for pervasive services
Proceedings of the 2009 international conference on Pervasive services
Adversary aware surveillance systems
IEEE Transactions on Information Forensics and Security
Identifying and facilitating social interaction with a wearable wireless sensor network
Personal and Ubiquitous Computing
Face detection through compact classifier using adaptive look-up-table
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Applying Commonsense Reasoning to Place Identification
International Journal of Handheld Computing Research
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The human dynamics group at the MIT Media Laboratory proposes that active pattern analysis of face-to-face interactions within the workplace can radically improve the functioning of the organization. There are several different types of information inherent in such conversations: interaction features, participants, context, and content. By aggregating this information, high-potential collaborations and expertise within the organization can be identified, and information efficiently distributed. Examples of using wearable machine perception to characterize face-to-face interactions and using the results to initiate productive connections are described, and privacy concerns are addressed.