Title: Ubiquitous sensor-based human behaviour recognition using the spatio-temporal representation of user states

Authors: Yoshinori Isoda, Shoji Kurakake, Kazuo Imai

Addresses: Development of Department of Service and Solution, NTT DoCoMo R&D Center, NTT DoCoMo, Inc., 3-5 Hikarino-Oka, Yokosuka-shi, Kanagawa 239-8536, Japan. ' Development of Department of Service and Solution, NTT DoCoMo R&D Center, NTT DoCoMo, Inc., 3-5 Hikarino-Oka, Yokosuka-shi, Kanagawa 239-8536, Japan. ' DoCoMo Communications Laboratories USA, Inc., 3240 Hillview Avenue Palo Alto, CA 94304-1201, USA

Abstract: There has been much research on location-based context-aware applications. However, any description of a person|s activities must include a temporal aspect as well as a location aspect. Therefore, it is important when creating enhanced user activity support systems to consider the user|s context in terms of spatio-temporal constraints. In this paper, we propose a user activity support system that employs a state sequence description scheme to describe the user|s context. In this scheme, each state is described as a spatio-temporal relationship between the user and objects. Typical sequences of states are stored as models of activities performed by a user. Each segment of user activities measured by the sensors and the Radio Frequency Identification tags (RFID tags) is classified into a state by using a decision tree constructed by the machine learning algorithm called C4.5. The user|s context is then obtained by matching the detected state series to a stored task model. To validate this system, we have developed an experimental house containing various embedded sensors and RFID-tagged objects. Having evaluated the performance of the proposed system, we conclude that our system is an effective way of acquiring the user|s spatio-temporal context.

Keywords: context recognition; decision tree; spatio-temporal representation; ubiquitous; human behaviour; behaviour recognition; user activity support systems; state sequence description; machine learning; sensors; radio frequency identification; RFID tags.

DOI: 10.1504/IJWMC.2008.019717

International Journal of Wireless and Mobile Computing, 2008 Vol.3 No.1/2, pp.46 - 55

Published online: 25 Jul 2008 *

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