Cross-people mobile-phone based activity recognition

Authors:
Zhongtang Zhao;Yiqiang Chen;Junfa Liu;Zhiqi Shen;Mingjie Liu
Affiliations:
Pervasive Computing Center, Institute of Computing Technology, CAS, Beijing,China and Graduate University of the Chinese Academy of Sciences, Beijing, China;Pervasive Computing Center, Institute of Computing Technology, CAS, Beijing,China and Nanyang Technological University, Singapore;Pervasive Computing Center, Institute of Computing Technology, CAS, Beijing,China and Nanyang Technological University, Singapore;Nanyang Technological University, Singapore;Pervasive Computing Center, Institute of Computing Technology, CAS, Beijing,China and Graduate University of the Chinese Academy of Sciences, Beijing, China
Venue:
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Year:
2011

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Abstract

Activity recognition using mobile phones has great potential in many applications including mobile healthcare. In order to let a person easily know whether he is in strict compliance with the doctor's exercise prescription and adjust his exercise amount accordingly, we can use a smart-phone based activity reporting system to accurately recognize a range of daily activities and report the duration of each activity. A triaxial accelerometer embedded in the smart phone is used for the classification of several activities, such as staying still, walking, running, and going upstairs and downstairs. The model learnt from a specific person often cannot yield accurate results when used on a different person. To solve the cross-people activity recognition problem, we propose an algorithm known as TransEMDT (Transfer learning EMbedded Decision Tree) that integrates a decision tree and the k-means clustering algorithm for personalized activity-recognition model adaptation. Tested on a real-world data set, the results show that our algorithm outperforms several traditional baseline algorithms.