A wavelet tensor fuzzy clustering scheme for multi-sensor human activity recognition. (April 2018)

• Record Type: Journal Article
• Authors: He, Hong; Tan, Yonghong; Zhang, Wuxiong
• Is Part Of: Engineering applications of artificial intelligence. Volume 70(2017:Oct.)
• Page Start: 109
• Page End: 122
• Publication Date: 2018-04
• Subjects: Activity recognition -- Dimensionality reduction -- Fuzzy clustering -- Wavelet packet transform -- Tensor; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.01.004
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: With the increasing number of wearable sensors and mobile devices, human activity recognition (HAR) based on multiple sensors has attracted more and more attention in recent years. On account of the diversity of human actions, the analysis of multivariate signals of activities is still a challenging task. Clustering is an unsupervised classification technique which can directly work on unlabeled data and automatically identify unknown activities. Therefore, a new wavelet tensor fuzzy clustering scheme (WTFCS) for multi-sensor activity recognition is proposed in this paper. Firstly, feature tensors of multiple activity signals are constructed using the discrete wavelet packet transform (DWPT). Then Multilinear Principal Component Analysis (MPCA) is utilized to reduce the dimensionality of feature tensors so as to keep the inherent data structure. On the basis of the principal feature initialization and the tensor fuzzy membership, a new fuzzy clustering (PTFC) is developed to identify different activity feature tensor groups. Finally, the open HAR dataset (DSAD) is used to verify the efficiency of the WTFCS. Clustering results of seventeen activities of eight subjects show that potential useful features of human activities can be captured through combining DWPT-based feature extraction with MPCA-based dimensionality reduction. The PTFC is capable of discriminating various human activities effectively. Its correctness rate of activity recognition is higher than those of fuzzy c-means clustering and the fuzzy clustering based on the tensor distance. Graphical abstract: Highlights: Proposed a wavelet tensor fuzzy clustering scheme (WTFCS) for activity recognition based on multiple sensor signals. Constructed feature tensors of multiple activity signals by the discrete wavelet packet transform. Reduced the dimensionality of activity feature tensors by Multilinear Principal Component Analysis. Developed a novel tensor fuzzy clustering algorithm based on the principal feature initialization and the tensor fuzzy membership. Clustering results of the open HAR dataset (DSAD) show that the WTFCS can discriminate various human activities effectively.