Unsupervised Activity Recognition with User's Physical Characteristics Data
ISWC '11 Proceedings of the 2011 15th Annual International Symposium on Wearable Computers
Modeling individual and group actions in meetings with layered HMMs
IEEE Transactions on Multimedia
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The new method proposed here recognizes activities performed by a group of users (e.g., attending a meeting, playing sports, and participating in a party) by using sensor data obtained from the users. Note that such group activities (GAs) have characteristics that differ from those of single user activities. For example, the number of users who participate in a GA is different for each activity. The number of meeting participants, for instance, may sometimes be different for each meeting. Also, a user may play different roles (e.g., `moderator' and `presenter' roles) in meetings on different days. We introduce the notion of role into our GA recognition model and try to capture the intrinsic characteristics of GAs with a hybrid unsupervised/supervised approach.