Telecommunication Systems

, Volume 52, Issue 4, pp 2299–2310 | Cite as

Towards an autonomous fall detection and alerting system on a mobile and pervasive environment

  • Ivo C. Lopes
  • Binod Vaidya
  • Joel J. P. C. RodriguesEmail author


In recent years, the use of sensors on mobile devices is highly desirable. In particular, an accelerometer can be used for numerous applications such as tracking object or monitoring of the elderly. This paper presents an application tool based on an accelerometer, call SensorFall to detect and report the acceleration caused by a fall, which allows sending alerts in the form of SMS, phone call, or by location using the GPS. We have implemented and verified the SensorFall in various environments, such as a hospital or a normal daily life for the elderly, also implemented the system calibration in order to adapt better the living conditions of each person. The results show that it performs well.


Mobile devices Sensors Accelerometer Fall detection 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yeoh, W.-S., Wu, J.-K., Pek, I., Yong, Y.-H., Chen, X., & Waluyo, A. B. (2008). Real-time tracking of flexion angle by using wearable accelerometer sensors. In 5th International workshop on wearable and implantable body sensor networks, Chinese University of Hong Kong, China, June 1–3. Google Scholar
  2. 2.
    Purwar, A., Jeong, D. U., & Chung, W. Y. (2007). Activity monitoring from real-time triaxial accelerometer data using sensor network. In International conference on control, automation and systems (ICCAS), Seoul, Korea, October 17–20. Google Scholar
  3. 3.
    Kannus, P., et al. (1999). Fall-induced injuries and deaths among older adults. JAMA, The Journal of the American Medical Association , 281(20), 1895–1899. CrossRefGoogle Scholar
  4. 4.
    Weir, E., & Culmer, L. (2004). Fall prevention in the elderly population. Canadian Medical Association Journal, 171(7). doi: 10.1503/cmaj.1041381.
  5. 5.
    Tinetti, M. E. (2003). Preventing falls in elderly persons. The New England Journal of Medicine, 348(1), 42–49. CrossRefGoogle Scholar
  6. 6.
    Kannus, P., Sievänen, H., Palvanen, M., Jarvinen, T., & Parkkari, J. (2005). Prevention of falls and consequent injuries in elderly people. The Lancet, 366(9500), 1885–1893. CrossRefGoogle Scholar
  7. 7.
    He, Z., Liu, Z., Jin, L., Zhen, L.-X., & Huang, J.-C. (2008). Weightlessness feature—a novel feature for single triaxial accelerometer based activity recognition. In 19th International conference on pattern recognition (ICPR), Tampa, FL, USA, December 8–11. Google Scholar
  8. 8.
    Kunze, K., & Lukowicz, P. (2007). Using acceleration signatures from everyday activities for on-body device location. In 11th IEEE international symposium on wearable computers (ISWC), Boston, MA, USA, October 11–13. Google Scholar
  9. 9.
    Palma, S., Silva, H., Gamboa, H., & Mil-Homens, P. (2008). Standing jump loft time measurement. In Biosignals, Madeira, Portugal, January 28–31. Google Scholar
  10. 10.
    Yeoh, W.-S., Pek, I., Yong, Y.-H., Chen, X., & Waluyo, A. B. (2008). Ambulatory monitoring of human posture and walking speed using wearable accelerometer sensors. In 30th Annual international conference of the IEEE engineering in medicine and biology society, Canada, Vancouver, August 20–24. Google Scholar
  11. 11.
    Lee, Y., Kim, J., Son, M., & Lee, M. (2007). Implementation of accelerometer sensor module and fall detection monitoring system based on wireless sensor network. In 29th Annual international conference of the IEEE EMBS, Lyon, France, August 23–26. Google Scholar
  12. 12.
    Chung, W.-Y., Bhardwaj, S., Punvar, A., Lee, D.-S., & Myllylae, R. (2007). A fusion health monitoring using ECG and accelerometer sensors for elderly persons at home. In 29th Annual international conference of the IEEE EMBS, Lyon, France, August 23–26. Google Scholar
  13. 13.
    Bang, S. L., Kim, M., Song, S.-K., & Park, S.-J. (2008). Toward real time detection of the basic living activity in home using a wearable sensor and smart home sensors. In 30th Annual international conference of the IEEE EMBS, Vancouver, British Columbia, Canada, August 20–24. Google Scholar
  14. 14.
    Gafurov, D., Snekkenes, E., & Bours, P. (2007). Gait authentication and identification using wearable accelerometer sensor. In 2007 IEEE workshop on automatic identification advanced technologies, Alghero, Italy, June 7–8. Google Scholar
  15. 15.
    Khan, A. M., Lee, Y. K., & Kim, T.-S. (2008). Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets. In 30th Annual international conference of the IEEE EMBS, Vancouver, British Columbia, Canada, August 20–24. Google Scholar
  16. 16.
    Gemignani, V., Bianchini, E., Faita, F., Giannoni, M., Pasanisi, E., Picano, E., & Bombardini, T. (2008). Assessment of cardiologic systole and diastole duration in exercise stress tests with a transcutaneous accelerometer sensor. In Computers in cardiology, Bologna, Italy, September 14–17. Google Scholar
  17. 17.
    Caporusso, N., Lasorsa, I., Rinaldi, O., & la Pietra, L. (2009). A pervasive solution for risk awareness in the context of fall prevention. In Pervasive health 2009, London, UK, April 1–3 (pp. 1–8). Google Scholar
  18. 18.
    Noury, N., Fleury, A., Rumeau, P., Bourke, A. K., Ó Laighin, G., & Rialle, V. (2007). Fall detection—principles and methods. In 28th Annual international conference of the IEEE EMBS, Lyon, France, August 23–28. Google Scholar
  19. 19.
    Huang, C.-N., Chiang, C.-Y., Chang, J.-S., Chou, Y.-C., Hong, Y.-X., Hsu, S. J., Chu, W.-C., & Chan, C.-T. (2009). Location-aware fall detection system for medical care quality improvement. In Third international conference on multimedia and ubiquitous engineering (MUE 2009), Qingdao, China, June 4–6 (pp. 477–480). CrossRefGoogle Scholar
  20. 20.
    Diuh, A., Teng, D., Chen, L., Shi, Y., McCrosky, C., Basran, J., & Del Bello-Hass, V. (2009). Implementation of a physical activity monitoring system for the elderly people with built-in vital sign and fall detection. In Sixth international conference on information technology: new generations, Las Vegas, NV, USA, April 27–29 (pp. 1226–1231). Google Scholar
  21. 21.
    Bianchi, F., Redmond, S. J., Narayanan, M. R., Cerutti, S., Celler, B. G., & Lovell, N. H. (2009). Falls event detection using triaxial accelerometry and barometric pressure measurement. In 31th Annual international IEEE EMBS conference, Minneapolis, Minnesota, USA, September 2–6. Google Scholar
  22. 22.
    Estudillo-Valderrama, M. À., Roa, L. M., Reina-Tosina, J., & Naranjo-Hernández, D. (2009). Design and implementation of a distributed fall detection system—personal server. IEEE Transactions on Information Technology in Biomedicine, 13(6), 874–881. CrossRefGoogle Scholar
  23. 23.
    Li, Q., Stankovic, J. A., Hanson, M. A., Barth, A. T., & Lach, J. (2009). Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In Sixth international workshop on wearable and implementable body sensor networks (BSN 2009), Berkeley, CA, USA, June 3–5 (pp. 138–143). CrossRefGoogle Scholar
  24. 24.
    Zhuang, X., Huang, J., Potaminanos, G., & Hasegawa-Johnson, M. (2009). Acoustic fall detection using Gaussian mixture models and GMM supervectors. In IEEE international conference on acoustics, speech, and signal processing (ICASSP), April 19–24 (pp. 69–72). Google Scholar
  25. 25.
    Zhang, T., Wang, J., Liu, P., & Hou, J. (2006). Fall detection by embedding an accelerometer in cellphone and using KFD algorithm. International Journal of Computer Science and Network Security, 6(10), 277–284. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Ivo C. Lopes
    • 2
  • Binod Vaidya
    • 1
  • Joel J. P. C. Rodrigues
    • 1
    • 2
    Email author
  1. 1.Instituto de TelecomunicaçõesCovilhãPortugal
  2. 2.Department of InformaticsUniversity of Beira InteriorCovilhãPortugal

Personalised recommendations