Hardware Design of the Energy Efficient Fall Detection Device

A. Skorodumovs 1 , E Avots 1 , J Hofmanis 1  und G. Korāts 1
  • 1 Ventspils University College, 101 Inzenieru Str., Ventspils, LV-3601, LATVIA


Health issues for elderly people may lead to different injuries obtained during simple activities of daily living. Potentially the most dangerous are unintentional falls that may be critical or even lethal to some patients due to the heavy injury risk. In the project “Wireless Sensor Systems in Telecare Application for Elderly People”, we have developed a robust fall detection algorithm for a wearable wireless sensor. To optimise the algorithm for hardware performance and test it in field, we have designed an accelerometer based wireless fall detector. Our main considerations were: a) functionality – so that the algorithm can be applied to the chosen hardware, and b) power efficiency – so that it can run for a very long time. We have picked and tested the parts, built a prototype, optimised the firmware for lowest consumption, tested the performance and measured the consumption parameters. In this paper, we discuss our design choices and present the results of our work.

Falls das inline PDF nicht korrekt dargestellt ist, können Sie das PDF hier herunterladen.

  • 4. Hornbrook, M. C., Stevens, V. J., Wingfield, D. J., Hollis, J. F., Greenlick, M. R., and M. G. Ory. (1994). Preventing falls among community-dwelling older persons: results from a randomized trial. The Gerontologist 34 (1), 16–23.

  • 5. Heinze, C., Halfens, R. J. G. and Dassen, T. (2007). Falls in German in-patients and residents over 65 years of age. Journal of Clinical Nursing16.3, 495–501.

  • 6. Brownsell, S.J., Bradley, D.A., Bragg, R., Catlin, P., and Carlier, J. (2000). Do community alarm users want telecare? Journal of Telemedicine and Telecare6, 199–204.

  • 7. Brownsell, S., and Hawley, M.S. (2004). Automatic fall detectors and the fear of falling. Journal of Telemedicine and Telecare 10, 262–6.

  • 8. Muhammad, M., Shao, L., and Seed, L. (2013). A survey on fall detection: Principles and approaches. Neurocomputing 100, 144–152.

  • 9. STMicroelectronics. (2011). LIS2DH ultra Low Power Accelerometer Datasheet [online] [accessed 8 September 2015]. Available athttp://www.st.com/web/en/resource/technical/document/datasheet/DM00042751.pdf.

  • 10. Atmel. (2014). ATxmega32C4 Microcontroller Datasheet [online] [accessed 8 September 2015]. Available at http://www.atmel.com/images/Atmel-8493-8-and-32-bit-AVRXMEGA-Microcontrollers-ATxmega16C4-ATxmega32C4_Datasheet.pdf.

  • 11. Nordic Semiconductor. (2007). nRF24L01+ 2.4GHz RF Module Product Specification [online] [accessed 8 September 2015]. Available at: http://www.nordicsemi.com/eng/nordic/download_resource/8041/1/88084864.

  • 12. MAXIM Integrated. (2013). MAX1724 Step-up Converter Datasheet [online] [accessed 8 September 2015]. Available at http://datasheets.maximintegrated.com/en/ds/MAX1722-MAX1724.pdf.

  • 13. Korats, G., Hofmanis, J., Skorodumovs, A., and Avots, E. (2015). Fall detection algorithm in energy efficient multistate sensor system. In: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS.

  • 14. VARTA. (2004). Industrial Alkaline AA Cell Datasheet [online] [accessed 8 September 2015]. Available at http://www.farnell.com/datasheets/627912.pdf.


Zeitschrift + Hefte