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Contactless monitoring of human behaviors in bed using RSSI signals

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Abstract

In this paper, contactless monitoring and classification of human activities and sleeping postures in bed using radio signals is presented. The major contribution of this work is the development of a contactless monitoring and classification system with a proposed framework that uses received signal strength indicator (RSSI) signals collected from only one wireless link, where different human activities and sleep postures, including (a) no one in the bed, (b) a man sitting on the bed, (c) sleeping on his back, (d) seizure sleeping, and (e) sleeping on his side, are tested. With our proposed system, there is no need to attach any sensors or medical devices to the human body or the bed. That is the limitation of the sensor-based technology. Additionally, our system does not raise a privacy concern, which is the major limitation of vision-based technology. Experiments using low-cost, low-power 2.4 GHz IEEE802.15.4 wireless networks have been conducted in laboratories. Results demonstrate that the proposed system can automatically monitor and classify human sleeping postures in real time. The average classification accuracy of activities and sleep postures obtained from different subjects, test environments, and hardware platforms is 99.92%, 98.87%, 98.01%, 87.57%, and 95.87% for cases (a) to (e), respectively. Here, the proposed system provides an average accuracy of 96.05%. Furthermore, the system can also monitor and separate the difference between the cases of the man falling from his bed and the man getting out of his bed. This autonomous system and sleep posture information can thus be used to support care people, physicians, and medical staffs in the evaluation and planning of treatment for the benefit of patients and related people.

Graphical Abstract

The proposed system for non-invasive monitoring and classification of human activities and sleeping postures in bed using RSSI signals

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Acknowledgements

The authors would like to thank Mr. Nattawut Na Songkhla for experimental support.

Funding

This work was supported by the Faculty of Engineering, Prince of Songkla University, Thailand, and the Division of Computer Engineering, The University of Aizu, Japan.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, 90110, Thailand

    Peeradon Thammachote, Chawakorn Intongkum, Kiattisak Sengchuai, Nattha Jindapetch, Pornchai Phukpattaranont & Apidet Booranawong

  2. Division of Computer Engineering, The University of Aizu, Aizu-Wakamatsu, 965-8580, Japan

    Hiroshi Saito

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  1. Peeradon Thammachote
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  2. Chawakorn Intongkum
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Contributions

Study conception and design: all authors; material preparation and data collection: PT, CI, KS, and AB; analysis and interpretation of results: PT, CI, KS, and AB; drafting of manuscript: PT and AB; critical revision of manuscript: NJ, PP, HS, and AB. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Apidet Booranawong.

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Thammachote, P., Intongkum, C., Sengchuai, K. et al. Contactless monitoring of human behaviors in bed using RSSI signals. Med Biol Eng Comput 61, 2561–2579 (2023). https://doi.org/10.1007/s11517-023-02847-6

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