一种用于鉴别体域网动作模式的近邻快速鲁棒协作表示分类算法

doi:10.3969/j.issn.0258-8021.2018.05.005

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Abstract This paper proposed a novel neighboring robust collaborative representation algorithm for wireless body area networks (WBANs)-based activity classification. Based on the similarity of multi-sensor action data structure, our proposed technique found out a few neighboring classes and samples associated with test sample according to the nearest neighbor principle. This allowed to construct the new training set for collaboratively representing action patterns. And then, the augmented lagrange multiplier algorithm was adopted to solve the representation coefficients and representation residuals of test sample, in order to significantly improve the classification performance. The multi-sensor action data are selected from an open wearable action recognition database (WARD) of University of California, Berkeley, in order to validate the effectiveness of our proposed technique. The results showed that our proposed method could capture more valuable correlation information associated with human action. The best accuracy was increased by 2% and the running time only spends 6.5 s, which suggested that our proposed technique was obviously superior to the sparse representation-based action classification algorithms. It is very helpful to offer a new powerful tool for recognizing action pattern in clinical application.

Key words： collaborative representation classification activity pattern classification wireless body sensor networks nearest neighbor

Received: 11 October 2017

PACS:

R318

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	Articles by authors
	Wu Jianning
	Ling Yun
	Wang Jiajing
	Lin Yingjie

Cite this article:

Wu Jianning,Ling Yun,Wang Jiajing, et al. A Novel Neighboring Collaborative Representation Classification Algorithm for Recognizing WBAN-Based Activity Pattern[J]. Chinese Journal of Biomedical Engineering, 2018, 37(5): 545-552.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2018.05.005 OR http://cjbme.csbme.org/EN/Y2018/V37/I5/545

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