Huaijun Wang
Wearable sensor-based human activity recognition using hybrid deep learning techniques
Wang, Huaijun; Zhao, Jing; Li, Junhuai; Tian, Ling; Tu, Pengjia; Cao, Ting; An, Yang; Wang, Kan; Li, Shancang
Authors
Jing Zhao
Junhuai Li
Ling Tian
Pengjia Tu
Ting Cao
Yang An
Kan Wang
Shancang Li Shancang.Li@uwe.ac.uk
Senior Lecturer in Computer Forensics and Security
Abstract
Human activity recognition (HAR) can be exploited to great benefits in many applications, including elder care, health care, rehabilitation, entertainment, and monitoring. Many existing techniques, such as deep learning, have been developed for specific activity recognition, but little for the recognition of the transitions between activities. This work proposes a deep learning based scheme that can recognize both specific activities and the transitions between two different activities of short duration and low frequency for health care applications. In this work, we first build a deep convolutional neural network (CNN) for extracting features from the data collected by sensors. Then, the long short-term memory (LTSM) network is used to capture long-term dependencies between two actions to further improve the HAR identification rate. By combing CNN and LSTM, a wearable sensor based model is proposed that can accurately recognize activities and their transitions. The experimental results show that the proposed approach can help improve the recognition rate up to 95.87% and the recognition rate for transitions higher than 80%, which are better than those of most existing similar models over the open HAPT dataset.
Citation
Wang, H., Zhao, J., Li, J., Tian, L., Tu, P., Cao, T., …Li, S. (2020). Wearable sensor-based human activity recognition using hybrid deep learning techniques. Security and Communication Networks, 2020, Article 2132138. https://doi.org/10.1155/2020/2132138
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 1, 2020 |
Online Publication Date | Jul 27, 2020 |
Publication Date | Jul 27, 2020 |
Deposit Date | Jul 28, 2020 |
Publicly Available Date | Mar 29, 2024 |
Journal | Security and Communication Networks |
Print ISSN | 1939-0114 |
Electronic ISSN | 1939-0122 |
Publisher | Hindawi |
Peer Reviewed | Peer Reviewed |
Volume | 2020 |
Article Number | 2132138 |
DOI | https://doi.org/10.1155/2020/2132138 |
Keywords | Computer Networks and Communications; Information Systems |
Public URL | https://uwe-repository.worktribe.com/output/6431559 |
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Wearable sensor-based human activity recognition using hybrid deep learning techniques
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Licence
http://creativecommons.org/licenses/by/4.0/
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
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