期刊
KNOWLEDGE-BASED SYSTEMS
卷 90, 期 -, 页码 138-152出版社
ELSEVIER
DOI: 10.1016/j.knosys.2015.09.024
关键词
Human activity recognition; Complex activity; Multilayer; Time series; Sensor
资金
- Cuiying Grant of China Telecom Gansu Branch [lzudxcy-2013-3]
- Science and Technology Planning Project of Chengguan District, Lanzhou [2013-3-1]
- National Natural Science Foundation of China [61370219]
- Chongqing Social Science Planning Fund Program [2014BS123]
- Fundamental Research Funds for the Central Universities in China [XDJK2014A002, XDJK2014C141, SWU114005]
- Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry [44th, 48th]
Human activity recognition can be exploited to benefit ubiquitous applications using sensors. Current research on sensor-based activity recognition is mainly using data-driven or knowledge-driven approaches. In terms of complex activity recognition, most data-driven approaches suffer from portability, extensibility and interpretability problems, whilst knowledge-driven approaches are often weak in handling intricate temporal data. To address these issues, we exploit time series shapelets for complex human activity recognition. In this paper, we first describe the association between activity and time series transformed from sensor data. Then, we present a recursively defined multilayered activity model to represent four types of activities and employ a shapelet-based framework to recognize various activities represented in the model.. A prototype system was implemented to evaluate our approach on two public datasets. We also conducted two real-world case studies for system evaluation: daily living activity recognition and basketball play activity recognition. The experimental results show that our approach is capable of handling complex activity effectively. The results are interpretable and accurate, and our approach is fast and energy-efficient in real-time. (C) 2015 Elsevier B.V. All rights reserved.
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