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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Human Activity Recognition Based on Multiple Kinects
verfasst von : Emanuela Haller, Georgiana Scarlat, Irina Mocanu, Mihai Trăscău
Erschienen in: Evaluating AAL Systems Through Competitive Benchmarking
Verlag: Springer Berlin Heidelberg
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Activity recognition is an important component for the ambient assisted living systems which perform home monitoring and assistance for elderly people or patients with risk factors. This paper presents a prototype system for activity recognition using information provided by four Kinects. First the posture of the supervised person is detected using a set of rules created with ID3 algorithm applied to a skeleton obtained by merging the skeletons provided by multiple Kinects. At the same time, the interaction of the user with the objects from the house is determined. After that, daily activities are identified using Hidden Markov Models in which the detected postures and the object interactions are observable states. The benefit of merging the information received from multiple Kinects together with the detection of the interaction between the user and relevant objects from the room is the increase in accuracy for the recognized activities.