Triaxial Accelerometer Located on the Wrist for Elderly People’s Fall Detection

  • Armando Collado Villaverde
  • María D. R-Moreno
  • David F. BarreroEmail author
  • Daniel Rodriguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9937)


The loss of motor function in the elderly makes this population group prone to accidental falls. Actually, falls are one of the most notable concerns in elder care. Not surprisingly, there are several technical solutions to detect falls, however, none of them has achieved great acceptance. The popularization of smartwatches provides a promising tool to address this problem. In this work, we present a solution that applies machine learning techniques to process the output of a smartwatch accelerometer, being able to detect a fall event with high accuracy. To this end, we simulated the two most common types of falls in elders, gathering acceleration data from the wrist, then applied that data to train two classifiers. The results show high accuracy and robust classifiers able to detect falls.


Fall detection Accelerometer Machine learning Classification Supervised learning Care for the elderly 



The authors thank the contribution of Isabel Pascual Benito, Francisco López Martínez and Helena Hernández Martínez, from Department of Nursing and Physiotherapy of the University of Alcalá, for their help designing and supervising the simulated falls procedure. This work is supported by UAH (2015/00297/001), JCLM (PEII-2014-015-A) and MINCECO (TIN2014-56494-C4-4-P).


  1. 1.
    Sadigh, S., Reimers, A., Andersson, R., Laflamme, L.: Falls and fall-related injuries among the elderly: a survey of residential-care facilities in a swedish municipality. J. Commun. Health 29, 129–140 (2004)CrossRefGoogle Scholar
  2. 2.
    Noury, N., Fleury, A., Rumeau, P., Bourke, A.K., Laighin, G.O., Rialle, V., Lundy, J.E.: Fall detection - principles and Methods. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1663–1666 (2007)Google Scholar
  3. 3.
    Gibson, R.M., Amira, A., Ramzan, N., Casaseca-de-la higuera, P., Pervez, Z.: Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic. Appl. Soft Comput. J. 39, 94–103 (2016)CrossRefGoogle Scholar
  4. 4.
    Luštrek, M., Kaluža, B.: Fall detection and activity recognition with machine learning. Informatica 33, 205–212 (2008)Google Scholar
  5. 5.
    Albert, M.V., Kording, K., Herrmann, M., Jayaraman, A.: Fall classification by machine learning using mobile phones. PLoS ONE 7, 3–8 (2012)Google Scholar
  6. 6.
    Zhou, H., Hu, H.: Reducing drifts in the inertial measurements of wrist and elbow positions. IEEE Trans. Instrum. Measur. 59, 575–585 (2010)CrossRefGoogle Scholar
  7. 7.
    Tao, Y., Hu, H., Zhou, H.: Integration of vision and inertial sensors for 3d arm motion tracking in home-based rehabilitation. Int. J. Robot. Res. 26, 607–624 (2007)CrossRefGoogle Scholar
  8. 8.
    Miaou, S.G., Sung, P.H., Huang, C.Y.: A customized human fall detection system using omni-camera images and personal information. In: Conference Proceedings - 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, D2H2 2006, pp. 39–42 (2006)Google Scholar
  9. 9.
    Cucchiara, R., Prati, A., Vezzani, R., Emilia, R.: A multi-camera vision system for fall detection and alarm generation. Expert Syst. 24, 334–345 (2007)CrossRefGoogle Scholar
  10. 10.
    Auvinet, E., Multon, F., Saint-Arnaud, A., Rousseau, J., Meunier, J.: Fall detection with multiple cameras: an occlusion-resistant method based on 3-D silhouette vertical distribution. IEEE Trans. Inf. Technol. Biomed. 15, 290–300 (2011)CrossRefGoogle Scholar
  11. 11.
    Zigel, Y., Litvak, D., Gannot, I.: A method for automatic fall detection of elderly people using floor vibrations and sound-Proof of concept on human mimicking doll falls. IEEE Trans. Biomed. Eng. 56, 2858–2867 (2009)CrossRefGoogle Scholar
  12. 12.
    Bagalà, F., Becker, C., Cappello, A., Chiari, L., Aminian, K., Hausdorff, J.M., Zijlstra, W., Klenk, J.: Evaluation of accelerometer-based algorithms on real-world falls. PloS one 7, e37062 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Armando Collado Villaverde
    • 1
  • María D. R-Moreno
    • 1
  • David F. Barrero
    • 1
    Email author
  • Daniel Rodriguez
    • 2
  1. 1.Departamento de AutomáticaUniversidad de AlcaláAlcalá de HenaresSpain
  2. 2.Departamento de Ciencias de la ComputaciónUniversidad de AlcaláAlcalá de HenaresSpain

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