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Latent feature learning for activity recognition using simple sensors in smart homes

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Abstract

Activity recognition is an important step towards monitoring and evaluating the functional health of an individual, and it potentially promotes human-centric ubiquitous applications in smart homes particularly for senior healthcare. The nature of human activity characterized by a high degree of complexity and uncertainty, however, poses a great challenge to the design of good feature representations and the optimization of classifiers towards building a robust model for human activity recognition. In this study, we propose to exploit deep learning techniques to automatically learn high-level features from the binary sensor data under the assumption that there exist discriminative latent patterns inherent in the simple low-level features. Specifically, we extract high-level features with a stacked autoencoder that has a deep and hierarchy architecture, and combine feature learning and classifier construction into a unified framework to obtain a jointly optimized activity recognizer. Besides, we investigate two different original feature representations of the sensor data for latent feature learning. To evaluate the performance of the proposed method, we conduct extensive experiments on three publicly available smart home datasets, and compare it with a range of shallow models in terms of time-slice accuracy and class accuracy. Experimental results show that our proposed model achieves better recognition rates and generalizes better across different original feature representations, indicating its applicability to the real-world activity recognition.

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Acknowledgements

This work was supported partially by the Natural Science Foundation of China (No. 61472057), the Fundamental Research Funds for the Central Universities (No. JZ2016HGBH1053), and the China Postdoctoral Science Foundation (No. 2016 M592046).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Chuzhou University, Chuzhou, 239000, China

    Guilin Chen & Shenghui Zhao

  2. School of Computer and Information, Hefei University of Technology, Hefei, 230009, China

    Aiguo Wang

  3. School of Software Engineering, Chongqing University, Chongqing, 400044, China

    Li Liu

  4. Department of Computer Science and Information Engineering, Tamkang University, Taipei, 25157, Taiwan

    Chih-Yung Chang

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  1. Guilin Chen
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  2. Aiguo Wang
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  3. Shenghui Zhao
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  4. Li Liu
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Correspondence to Aiguo Wang.

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Chen, G., Wang, A., Zhao, S. et al. Latent feature learning for activity recognition using simple sensors in smart homes. Multimed Tools Appl 77, 15201–15219 (2018). https://doi.org/10.1007/s11042-017-5100-4

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  • DOI: https://doi.org/10.1007/s11042-017-5100-4

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