Wearable Device-based Fall Detection System for Elderly Care Using Support Vector Machine (SVM) classifier

  • Authors

    • Nur Syazarin Natasha Abd Aziz
    • Salwani Mohd Daud
    • Nurul Iman Mohd Sa’at
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.36.23924
  • fall detection, elderly, Support Vector Machine, wearable

  • Abstract

    Fall is an increasing problem as people ageing. It may happen to anyone, but their incidence does increase with age. Hence, the elderly will be facing catastrophic consequences due to falls. Nevertheless, there are still vulnerable in its accuracy in categorizing and differentiating the Activities Daily Living (ADL) and falls as most of the existing systems cause false alarm. This paper presents the research and simulation of wearable device-based fall detection approach by addressing the building of wearable device-based fall detection system for elderly care by using mobile devices. Two main phases involve in this research: online phase and offline phase. Online phase covers in data acquisition step whereby the raw data of simulated fall by participants is collected via built-in-tri-axial accelerometer in a smartphone, then automatically sent towards the computer via wireless communication. Meanwhile, offline phase covers data pre-processing, feature extraction and selection and data classification where these steps are handled in offline mode. Support Vector Machine (SVM) classifier was employed, and evaluated in the analysis. Overall accuracy rate, sensitivity, specificity as well as False Positive Rate (FPR) and False Negative Rate (FNR) were calculated. The findings suggest that SVM with Polynomial (order 5) method which achieved 68.91% overall accuracy as well as producing only 24.46% FPR is the most precise model for fall detection system in this paper. This approach has the potential to be implemented and deploy in real mobile application in future.   

  • References

    1. [1] Vaught, S. L. (2001). Gait, Balance, and Fall Prevention. Ochsner Clinic and Alton Ochsner Medical Foundation, 94-97.

      [2] Kerdegari, H., Samsudin, K., Ramli, A., & Mokaram, S. (2011). Evaluation of fall detection classification approaches. International Conference on Intelligent and Advanced Systems (pp. 131-136). IEEE.

      [3] M, Rizawati., & S, Mas Ayu. (2008). Home environment and fall at home among the elderly in Masjid Tanah province. JUMMEC, 72-82.

      [4] Yahaya, N., Abdullah, S., Momtaz, Y. A., & Hamid, T. A. (2010). Quality of life of older Malaysians living alone. Educational Gerontology, 893-906.

      [5] Muhammad Mubashir, Ling, S., & Seed, L. (2012). A survey on fall detection: Principles and approaches. Neurocomputing, 144-152.

      [6] Aw, K., Ahmad, A., B. Zaman, H., & Sulaiman, L. (2014). A wearable device for elderly: A case study in Malaysia. International Conference on Information Technology and Multimedia (pp. 318-323). Putrajaya: IEEE.

      [7] Zaw, Z. H., Egerton, S., & Kuang, Y. C. (2011). A monocular view-invariant fall detection system for the elderly in assisted home environment. International Conference on Intelligent Environments (pp. 40-46). IEEE.

      [8] Backere, F. D., Ongenae, F., Van den Abeele, F., Nelis, J., Bonte, P., Clement, E., et al. (2015). Towards a social and context-aware multi-sensor fall detection and risk assessment platform. Computers in Biology and Medicine, 307-320.

      [9] Senden, R., Savelberg, H., Grimm, B., Heyligers, I., & Meijer, K. (2012). Accelerometry-based gait analysis, an additional objective approach to screen subjects at risk for falling. Gait & Posture, 296-300.

      [10] Samsudeen, A., Sulphey, M.M. “Does performance management of medical practitioners stand apart?â€,(2018) International Journal of Pharmaceutical Research, 10 (2), pp. 101-106.

      [11] Aziz, O., Musngi, M., Park, E. J., Mori, G., & Robinvitch, S. N. (2016). A comparison of accuracy of fall detection algorithms using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-falls trials. International Federation for Medical and Biological Engineering.

      [12] Abbate, S., Avvenuti, M., Bonatesta, F., Cola, G., Corsini, P., & Vecchio, A. (2012). A smartphone-based fall detection system. Pervasive and Mobile Computing, 883-899.

  • Downloads

  • How to Cite

    Syazarin Natasha Abd Aziz, N., Mohd Daud, S., & Iman Mohd Sa’at, N. (2018). Wearable Device-based Fall Detection System for Elderly Care Using Support Vector Machine (SVM) classifier. International Journal of Engineering & Technology, 7(4.36), 488-491. https://doi.org/10.14419/ijet.v7i4.36.23924

    Received date: 2018-12-14

    Accepted date: 2018-12-14

    Published date: 2018-12-09