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A lightweight fast human activity recognition method using hybrid unsupervised-supervised feature

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Abstract

The rapid growth of the elderly population has become an important issue for today’s society. Ambient-assisted living is a new means that focuses on providing assistance to address the needs of elderly people. Mobile device-based human activity recognition (HAR) has drawn increasing attention in this field. However, there are still many issues in HAR that remain open. Most existing HAR methods have overlooked the fact that the resources (CPU and memory) of mobile devices are limited. In view of this, a lightweight fast learning method based on stochastic configuration networks (SCNs) and dynamic stepwise updating technology was presented in this paper, which contributed to the reduction of the computational complexity and memory consumption for modeling. Besides, a hybrid unsupervised-supervised feature selection approach was proposed to obtain a feature set with high separability and low redundancy. The experimental results demonstrated that the proposed HAR method achieved lower resource consumption compared to other methods for human activity recognition.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61973306, in part by the Nature Science Foundation of Jiangsu Province under Grant BK20200086, in part by the Open Project Foundation of State Key Laboratory of Process Automation in Mining & Metallurgy under BGRIMM-KZSKL-2021-11, in part by the Assistance Program for Future Outstanding Talents of China University of Mining and Technology under Grant 2022WLKXJ077, in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant KYCX22_2552.

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Authors and Affiliations

  1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China

    Jing Nan, Chuanfeng Ning & Wei Dai

  2. State Key Laboratory of Process Automation in Mining & Metallurgy, Beijing, 100000, China

    Gang Yu

  3. Beijing Key Laboratory of Process Automation in Mining & Metallurgy, Beijing, 100000, China

    Gang Yu

  4. Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China

    Jing Nan, Chuanfeng Ning & Wei Dai

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  1. Jing Nan
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Correspondence to Wei Dai.

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Nan, J., Ning, C., Yu, G. et al. A lightweight fast human activity recognition method using hybrid unsupervised-supervised feature. Neural Comput & Applic 35, 10109–10121 (2023). https://doi.org/10.1007/s00521-023-08368-5

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