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Smart Fall Prediction for Elderly Care Using iPhone and Apple Watch

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Abstract

Along with the annually increasing elderly population, the issues particularly regarding the elderly care are gaining inevitable attention. Nursing and taking care of the elderly people is now the emphasized issue. Besides, the social phenomena of large urban–rural gap and population emigration cause serious shortage of nursing manpower. Among the needs for good care, falls present extreme risks in the elderly population. The shortage of nursing manpower causes the impossible provision of real-time-care for the elderly fall accidents. This study uses the three-axis accelerometer in Apple watch to measure the movement and to input it to the High-Level Fuzzy Petri Net (HLFPN) prediction model. Based on the HLFPN model, fuzzy reasoning is performed to predict the users’ daily motions including walking, sitting down, and falls. With the application to elderly exercise model, this system would transmit real-time positioning datasets when there are falls for the rapid and proper treatment so as to minimize the elderly injury caused by falls. Based on the experimental datasets regarding forward fall, backward fall, right-sided fall, and left-sided fall, the obtained precision values are 95.45%, 97.72%, 94.67%, and 95.12%, respectively. Walking, running, and falls could also be distinguished in this study. It is expected that this study could assist in solving the problem of nursing manpower shortage, providing immediate assistance for the elderly on the occasion of falls.

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Acknowledgments

This work was supported by the Ministry of Science and Technology, Taiwan, ROC under its Grant MOST 107-2221-E-029-029.

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Authors and Affiliations

  1. Department of Information Management, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei City, 106, Taiwan, ROC

    Kuang-Yen Tai & Frank Yeong-Sung Lin

  2. Graduate Institute of Biomedical and Bioinformatics Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei City, 106, Taiwan, ROC

    Dai-Lun Chiang & Feipei Lai

  3. Department of Information Management, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Taichung City, 407, Taiwan

    Tzer-Shyong Chen

  4. Department of Computer Science and Information Engineering, National Taipei University, 151, University Rd., Sanhsia District, New Taipei City, 237, Taiwan

    Victor R. L. Shen

  5. Department of Information Management, Chaoyang University of Technology, 168, Jifeng E. RdWufeng District, Taichung City, 413, Taiwan

    Victor R. L. Shen

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  1. Kuang-Yen Tai
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  2. Dai-Lun Chiang
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  3. Tzer-Shyong Chen
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  4. Victor R. L. Shen
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  6. Frank Yeong-Sung Lin
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Correspondence to Victor R. L. Shen.

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Tai, KY., Chiang, DL., Chen, TS. et al. Smart Fall Prediction for Elderly Care Using iPhone and Apple Watch. Wireless Pers Commun 114, 347–365 (2020). https://doi.org/10.1007/s11277-020-07366-3

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  • DOI: https://doi.org/10.1007/s11277-020-07366-3

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