Accelerometer Sensor Based Fall Sensing for Elderly

Authors

  • Anas Mohd Noor Bioinstrumentation Research Laboratory,School of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau, Perlis
  • Hafizudin Zainudin Biomedical Electronic Engineering, Schoool of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau,Perlis
  • Normaheran Hanafi Biomedical Electronic Engineering, Schoool of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau,Perlis
  • Siti Aishah Baharuddin Biomedical Electronic Engineering, Schoool of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau,Perlis
  • Mohamad Aliff Abdul Rahim Biomedical Electronic Engineering, Schoool of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau,Perlis

DOI:

https://doi.org/10.11113/jt.v73.3162

Keywords:

Fall detection, accelerometer, real-time monitoring, GSM notification

Abstract

Fall can be recognized as an abnormal or action of losing an upright motion which will cause people especially elderly to suffer from pain and more seriously can affect one’s health. Being able to detect fall is key parameter to decrease the risk of severe injury to the seniors. There are such existing fall detection products on the market to assist elderly so that immediate response could be taken. However, due to complexity system, high cost and employing outside technology, these products initiate limitations such as maintenance and system enhancement. In this project, a fall detection device and system is developed using local technology, simple and cost effective. The prototype system consist of accelerometer sensing circuit, microcontroller with wireless signal transmission, Global System for Mobile Communications (GSM) notification alert for mobile phone and graphical user interface (GUI) to obtain real-time monitoring. The simple fall detection algorithm is developed to ensure false detection could be minimized. The overall performance of the developed device and system is proven reliable and practical.

 

Author Biographies

  • Anas Mohd Noor, Bioinstrumentation Research Laboratory,School of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau, Perlis
    With School of Mechatronic Engineering UniMAP
  • Hafizudin Zainudin, Biomedical Electronic Engineering, Schoool of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau,Perlis
    Final Year Student of Biomedical Engineering, UniMAP
  • Normaheran Hanafi, Biomedical Electronic Engineering, Schoool of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau,Perlis
    Final Year Student of Biomedical Engineering, UniMAP
  • Siti Aishah Baharuddin, Biomedical Electronic Engineering, Schoool of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau,Perlis
    Final Year Student of Biomedical Engineering, UniMAP
  • Mohamad Aliff Abdul Rahim, Biomedical Electronic Engineering, Schoool of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau,Perlis
    Final Year Student of Biomedical Engineering, UniMAP

References

Gibson, M., Andres, R., Isaacs, B., Radebaugh, T. & Worm-Petersen, J. 1987. The prevention of falls in later life. a report of the kellogg international work group on the prevention of falls by the elderly. Danish Medical Bulletin. 34(4): 1–24.

Goh Yongli, Ooi Shih Yin and Pang Ying Han. 2012. State of the Art: A Study on Fall Detection. World Academy of Science, Engineering and Technology.

Charalampos Doukas , Ilias Maglogiannis , Philippos Tragas , Dimitris Liapis , Gregory Yovanof. Patient Fall Detection using Support Vector Machines.

Annekenny, R., O’Shea, D. 2002. Falls and syncope in elderly patients. Clinics in Geriatric Medicine 18 (2): xiii–xiv.

McIntosh, S., Lawson, J., Kenny, R.A. 1993. Clinical characteristics of vasodepressor, cardio-inhibitory, and mixed carotid sinus syndrome in the elderly. The American Journal of Medicine. 95 (2): 203–208.

M. N. Nyan, Francis E. H. Tay, and E. Murugasu. 2008. A wearable system for pre-impact fall detection. Journal of Biomechanics. 41(16): 3475–3481.

Xinguo Yu. 2008. Approaches and principles of fall detection for elderly and patient. IEEE.

Doughty, K., Lewis, R., & McIntosh, A. 2000. The design of a practical and reliable fall detector for community and institutional telecare. Journal of Telemedicine and Telecare. 6(1): 150–154.

Bourke, A. K., O’brien, J. V., & Lyons, G. M. 2007. Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait & posture. 26(2): 194–199.

Li, Q., Stankovic, J. A., Hanson, M. A., Barth, A. T., Lach, J., & Zhou, G. 2009. Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In Wearable and Implantable Body Sensor Networks. IEEE. 2009. BSN 2009. Sixth International Workshop. 138–143.

Srinivasan, S., Han, J., Lal, D., & Gacic, A. 2007. Towards automatic detection of falls using wireless sensors. In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE. 1379–1382.

Nugent, C., Boytsov, A., Hallberg, J., Synnes, K.Synnes, S. McClean, and D. Finlay. 2013. Optimal Placement of Accelerometers for the Detection of Everyday Activities. Sensors. 13(7): 9183–9200.

Lindemann, U., Hock, A., Stuber, M., Keck, W., & Becker, C. 2005. Evaluation of a fall detector based on accelerometers: A pilot study. Medical and Biological Engineering and Computing. 43(5): 548–551.

Kangas, M., Konttila, A., Lindgren, P., Winblad, I., & Jämsä, T. 2008. Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait & Posture. 28(2): 285–291.

Downloads

Published

2015-02-10

Issue

Section

Science and Engineering

How to Cite

Accelerometer Sensor Based Fall Sensing for Elderly. (2015). Jurnal Teknologi, 73(1). https://doi.org/10.11113/jt.v73.3162