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WMNN: Wearables-Based Multi-Column Neural Network for Human Activity Recognition.

Accepted version
Peer-reviewed

Change log

Authors

Chen, Xuhang 
Gong, Jing 
Occhipinti, Luigi G  ORCID logo  https://orcid.org/0000-0002-9067-2534

Abstract

In recent years, human activity recognition (HAR) technologies in e-health have triggered broad interest. In literature, mainstream works focus on the body's spatial information (i.e. postures) which lacks the interpretation of key bioinformatics associated with movements, limiting the use in applications requiring comprehensively evaluating motion tasks' correctness. To address the issue, in this article, a Wearables-based Multi-column Neural Network (WMNN) for HAR based on multi-sensor fusion and deep learning is presented. Here, the Tai Chi Eight Methods were utilized as an example as in which both postures and muscle activity strengths are significant. The research work was validated by recruiting 14 subjects in total, and we experimentally show 96.9% and 92.5% accuracy for training and testing, for a total of 144 postures and corresponding muscle activities. The method is then provided with a human-machine interface (HMI), which returns users with motion suggestions (i.e. postures and muscle strength). The report demonstrates that the proposed HAR technique can enhance users' self-training efficiency, potentially promoting the development of the HAR area.

Description

Keywords

Humans, Neural Networks, Computer, Human Activities, Movement, Motion, Wearable Electronic Devices

Journal Title

IEEE J Biomed Health Inform

Conference Name

Journal ISSN

2168-2194
2168-2208

Volume Title

Publisher

Institute of Electrical and Electronics Engineers (IEEE)