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A Night-Time Monitoring System (eNightLog) to Prevent Elderly Wandering in Hostels: A Three-Month Field Study

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  • James Chung-Wai Cheung

    (Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
    Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong 999077, China
    Jockey Club Smart Ageing Hub, Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China)

  • Eric Wing-Cheung Tam

    (Jockey Club Smart Ageing Hub, Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China)

  • Alex Hing-Yin Mak

    (Jockey Club Smart Ageing Hub, Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China)

  • Tim Tin-Chun Chan

    (Jockey Club Smart Ageing Hub, Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China)

  • Yong-Ping Zheng

    (Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
    Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong 999077, China
    Jockey Club Smart Ageing Hub, Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China)

Abstract

Older people are increasingly dependent on others to support their daily activities due to geriatric symptoms such as dementia. Some of them stay in long-term care facilities. Elderly people with night wandering behaviour may lose their way, leading to a significant risk of injuries. The eNightLog system was developed to monitor the night-time bedside activities of older people in order to help them cope with this issue. It comprises a 3D time-of-flight near-infrared sensor and an ultra-wideband sensor for detecting human presence and to determine postures without a video camera. A threshold-based algorithm was developed to classify different activities, such as leaving the bed. The system is able to send alarm messages to caregivers if an elderly user performs undesirable activities. In this study, 17 sets of eNightLog systems were installed in an elderly hostel with 17 beds in 9 bedrooms. During the three-month field test, 26 older people with different periods of stay were included in the study. The accuracy, sensitivity and specificity of detecting non-assisted bed-leaving events was 99.8%, 100%, and 99.6%, respectively. There were only three false alarms out of 2762 bed-exiting events. Our results demonstrated that the eNightLog system is sufficiently accurate to be applied in the hostel environment. Machine learning with instance segmentation and online learning will enable the system to be used for widely different environments and people, with improvements to be made in future studies.

Suggested Citation

  • James Chung-Wai Cheung & Eric Wing-Cheung Tam & Alex Hing-Yin Mak & Tim Tin-Chun Chan & Yong-Ping Zheng, 2022. "A Night-Time Monitoring System (eNightLog) to Prevent Elderly Wandering in Hostels: A Three-Month Field Study," IJERPH, MDPI, vol. 19(4), pages 1-16, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2103-:d:748384
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    References listed on IDEAS

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    1. Hedi Hofmann & Sabine Hahn, 2014. "Characteristics of nursing home residents and physical restraint: a systematic literature review," Journal of Clinical Nursing, John Wiley & Sons, vol. 23(21-22), pages 3012-3024, November.
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    1. Hyo-Jung Lim & Derek Ka-Hei Lai & Bryan Pak-Hei So & Calvin Chi-Kong Yip & Daphne Sze Ki Cheung & James Chung-Wai Cheung & Duo Wai-Chi Wong, 2023. "A Comprehensive Assessment Protocol for Swallowing (CAPS): Paving the Way towards Computer-Aided Dysphagia Screening," IJERPH, MDPI, vol. 20(4), pages 1-9, February.
    2. Andy Yiu-Chau Tam & Li-Wen Zha & Bryan Pak-Hei So & Derek Ka-Hei Lai & Ye-Jiao Mao & Hyo-Jung Lim & Duo Wai-Chi Wong & James Chung-Wai Cheung, 2022. "Depth-Camera-Based Under-Blanket Sleep Posture Classification Using Anatomical Landmark-Guided Deep Learning Model," IJERPH, MDPI, vol. 19(20), pages 1-12, October.

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