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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

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Huaijun Wang

Jing Zhao

Junhuai Li

Ling Tian

Pengjia Tu

Ting Cao

Yang An

Kan Wang

Shancang Li


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.


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.

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 Jul 28, 2020
Journal Security and Communication Networks
Print ISSN 1939-0114
Electronic ISSN 1939-0122
Publisher Hindawi
Peer Reviewed Peer Reviewed
Volume 2020
Article Number 2132138
Keywords Computer Networks and Communications; Information Systems
Public URL


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