Title: Broad learning system for human activity recognition using sensor data

Authors: Ai-Qiang Yang; Xing-Hong Yu; Ting-Li Su; Xue-Bo Jin; Jian-Lei Kong

Addresses: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China ' School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China ' School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China ' School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China ' School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China

Abstract: In a multi-sensor environment, it is efficient to record and reflect people's information of activities, using the large amount of data. However, the data cannot directly display the form of activity itself so that it is necessary to do the further job of exploration and processing. Deep Learning (DL) methods have attracted more attention and have shown some superior performance, while they have the problem of structural complexity. Therefore, this paper creatively uses Broad Learning System (BLS) method for human activity recognition. We use sliding window to get the data segmented. The weights involved are fine-tuned by pseudo-inverse and ridge regression algorithms, and we achieve an accurate classification of activities. The method is verified by using OPPORTUNITY data set. The results show that this method can greatly shorten the learning time and improve the accuracy, as well as the performance in comparison with traditional methods.

Keywords: broad learning system; human activity recognition; sensor data; sliding window processing.

DOI: 10.1504/IJCAT.2019.103297

International Journal of Computer Applications in Technology, 2019 Vol.61 No.4, pp.259 - 264

Received: 10 Jan 2019
Accepted: 07 Mar 2019
Published online: 25 Oct 2019 *

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