A low-cost machine learning process for gait measurement using an electrostatic sensors network

Authors

  • Blandine Pichon
  • Eric Benoit Université Savoie Mont Blanc, LISTIC, Annecy
  • Stéphane Perrin Université Savoie Mont Blanc, LISTIC, Annecy
  • Alexandre Benoit Université Savoie Mont Blanc, LISTIC, Annecy
  • Nicolas Berton Université Savoie Mont Blanc, LISTIC, Annecy
  • Dorian Coves Université Savoie Mont Blanc, LISTIC, Annecy
  • Julien Cruvieux Université Savoie Mont Blanc, LISTIC, Annecy
  • Youssouph Faye Université Savoie Mont Blanc, LISTIC, Annecy

DOI:

https://doi.org/10.21014/actaimeko.v13i1.1323

Keywords:

machine learning, electrostatic sensor, PIR, depth camera, home monitoring, gait measurement

Abstract

Continuous in-house measurement of gait of elderly People is relevant for health professionals. To be adopted by most, the system must be low-cost and non-intrusive. In this paper we present a solution for measuring the walking velocity based on a network of 4 electric potential sensors. In our experiments, we also add PIR sensors used in our previous work for comparative purposes. A temporary Depth camera is used for training the model on walking velocity. The first results presented are obtained without machine learning. Then a machine learning regression method is tested to reduce the uncertainty of the sensors. The results show that the electric potential sensors are suitable for the in-house measurement of walking speed of elderly people. The uncertainty is lower than the target of 0.15 m s-1 known as the upper limit for detecting a reduction in speed due to illness. As for the PIR sensors, electric potential sensors consume very little energy, they are inexpensive, they can be embedded and hidden in the home which makes them less -intrusive and furthermore have better accuracy.

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Published

2024-03-26

Issue

Vol. 13 No. 1 (2024)

Section

Research Papers

License

Copyright (c) 2024 Blandine Pichon, Eric Benoit, Stéphane Perrin, Alexandre Benoit, Nicolas Berton, Dorian Coves, Julien Cruvieux, Youssouph Faye

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