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Motion and Location-Based Online Human Daily Activity Recognition

Motion and Location-Based Online Human Daily Activity Recognition

Chun Zhu, Weihua Sheng
Copyright: © 2013 |Pages: 18
ISBN13: 9781466636828|ISBN10: 1466636823|EISBN13: 9781466636835
DOI: 10.4018/978-1-4666-3682-8.ch015
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MLA

Zhu, Chun, and Weihua Sheng. "Motion and Location-Based Online Human Daily Activity Recognition." Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security, edited by Hans W. Guesgen and Stephen Marsland, IGI Global, 2013, pp. 304-321. https://doi.org/10.4018/978-1-4666-3682-8.ch015

APA

Zhu, C. & Sheng, W. (2013). Motion and Location-Based Online Human Daily Activity Recognition. In H. Guesgen & S. Marsland (Eds.), Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security (pp. 304-321). IGI Global. https://doi.org/10.4018/978-1-4666-3682-8.ch015

Chicago

Zhu, Chun, and Weihua Sheng. "Motion and Location-Based Online Human Daily Activity Recognition." In Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security, edited by Hans W. Guesgen and Stephen Marsland, 304-321. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3682-8.ch015

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Abstract

In this chapter, the authors propose an approach to indoor human daily activity recognition that combines motion data and location information. One inertial sensor is worn on the thigh of a human subject to provide motion data while a motion capture system is used to record the human location information. Such a combination has the advantage of significantly reducing the obtrusiveness to the human subject at a moderate cost of vision processing, while maintaining a high accuracy of recognition. The approach has two phases. First, a two-step algorithm is proposed to recognize the activity based on motion data only. In the coarse-grained classification, two neural networks are used to classify the basic activities. In the fine-grained classification, the sequence of activities is modeled by a Hidden Markov Model (HMM) to consider the sequential constraints. The modified short-time Viterbi algorithm is used for real-time daily activity recognition. Second, to fuse the motion data with the location information, Bayes’ theorem is used to refine the activities recognized from the motion data. The authors conduct experiments in a mock apartment, and the obtained results prove the effectiveness and accuracy of the algorithms.

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