Skip to main content

Advertisement

Springer Nature Link
Log in

Smart Fall Prediction for Elderly Care Using iPhone and Apple Watch

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Kalache, A. F. D., Yoshida, S. (2008). WHO global report on falls prevention in older age.

  2. Nicola, V., Pinar, S., Brendon, S., Stefania, M., Sarah, J. E., Jacopo, D., et al. (2019). Dietary protein intake and falls in older people: Longitudinal analyses from the osteoarthritis initiative. Journal of the American Medical Directors Association, 20(12), 1623–1627.

    Article  Google Scholar 

  3. Liu, G. L., et al. (2018). Human action recognition using a distributed RGB-depth camera network. IEEE Sensors Journal, 18(18), 7570–7576.

    Article  Google Scholar 

  4. Mlinac, M. E., & Feng, M. C. (2016). Assessment of activities of daily living, self-care, and independence. Archives of Clinical Neuropsychology, 31(6), 506–516.

    Article  Google Scholar 

  5. Kwon, S. B., Park, J. H., Kwon, C., Kong, H. J., Hwang, J. Y., & Kim, H. C. (2019). An energy-efficient algorithm for classification of fall types using a wearable sensor. IEEE Access, 7, 31321–31329.

    Article  Google Scholar 

  6. Lee, J. S., & Tseng, H. H. (2019). Development of an enhanced threshold-based fall detection system using smartphones with built-in accelerometers. IEEE Sensors Journal, 19(18), 8293–8302.

    Article  Google Scholar 

  7. Rakhman, A. Z., et al. (2014). Fall detection system using accelerometer and gyroscope based on smartphone. In Procs. of the 1st international conference on information technology, computer, and electrical engineering.

  8. You, D., Wang, S. G., & Seatzu, C. (2019). Verification of fault-predictability in labeled Petri nets using predictor graphs. IEEE Transactions on Automatic Control, 64(10), 4353–4360.

    Article  MathSciNet  Google Scholar 

  9. Ashraf, J., et al. (2018). Analyzing the behavior of neuronal pathways in Alzheimer's disease using Petri net modeling approach. Frontiers in Neuroinformatics, 12, 101–111.

    Article  Google Scholar 

  10. Shen, V. R. L., Lai, H. Y., & Lai, A. F. (2015). The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net. Applied Soft Computing, 26, 390–400.

    Article  Google Scholar 

  11. Liu, K. C., Hsieh, C. Y., Steen, J. H., & Chan, C. T. (2018). Impact of sampling rate on wearable-based fall detection systems based on machine learning models. IEEE Sensors Journal, 18(23), 9882–9890.

    Article  Google Scholar 

  12. Shen, R. K., et al. (2017). A novel fall prediction system on smartphones. IEEE Sensors Journal, 17(6), 1865–1871.

    Article  Google Scholar 

  13. Malekian, R., Kavishe, A. F., Maharaj, B. T., Gupta, P. K., Singh, G., & Waschefort, H. (2016). Smart vehicle navigation system using hidden Markov model and RFID technology. Wireless Personal Communications, 90(4), 1717–1742.

    Article  Google Scholar 

  14. Malche, T., Maheshwary, P., & Kumar, R. (2019). Environmental monitoring system for smart city based on secure Internet of Things (IoT) architecture. Wireless Personal Communications, 107, 2142–2172.

    Article  Google Scholar 

  15. Kingsy Grace, R., & Manju, S. (2019). A comprehensive review of wireless sensor networks based air pollution monitoring systems. Wireless Personal Communications, 108, 2499–2515.

    Article  Google Scholar 

  16. Jaiswal, R. K., & Jaidhar, C. D. (2018). A performance evaluation of location prediction position-based routing using real GPS traces for VANET. Wireless Personal Communications, 108, 275–292.

Download references

Acknowledgments

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

Author information

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

Authors
  1. Kuang-Yen Tai
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Dai-Lun Chiang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Tzer-Shyong Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Victor R. L. Shen
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Feipei Lai
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Frank Yeong-Sung Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Victor R. L. Shen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

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

Keywords