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Möllering, S., Ziefle, M., Schmitz-Rode, T. and Klack, L. (2010) Future Care Floor: A Sensitive Floor for Movement Monitoring and Fall Detection in Home Environments. In: Lin, J.C. and Nikita, K.S., Eds., Wireless Mobile Communication and Healthcare. MobiHealth 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer, Berlin, Heidelberg, 211-218.
https://doi.org/10.1007/978-3-642-20865-2_27

has been cited by the following article:

• TITLE: Design of a Smart Sole with Advanced Fall Detection Algorithm

  AUTHORS: Mostapha Zitouni, Qiang Pan, Damien Brulin, Eric Campo

  KEYWORDS: Fall Detection, Elderly, Accelerometer, Smart Insole, Classification

  JOURNAL NAME: Journal of Sensor Technology, Vol.9 No.4, December 12, 2019

  ABSTRACT: Fall has become the second leading cause of unintentional injury, death, after road traffic injuries, for the elderly in Europe. This proportion will increase in the next decades and become more than ever a real public health issue. France was selected by the World Health Organization to be the first country to implement a program that reduces the coverage of the dependence. Commercial automatic fall detection devices can help seniors get back on their feet faster by reducing the time of emergency procedure. Many seniors do not take advantage of this potentially life-saving technology mainly because of intrusiveness constraints. After having reminded the context and the challenges of fall detection systems, this paper presents an original device which is unobtrusive, comfortable and very effective. The hardware architecture embedded into the sole and a new fall detection algorithm based on acceleration and time thresholds are presented. The algorithm introduces a new concept of differential acceleration to eliminate some drawbacks of current systems. Tests were carried out under real life conditions by 6 young participants for different ADLs. The data were analyzed blindly. We compared the detected falls and found a 100% sensibility and more than 93% sensitivity for all participants and scenarios.