The risk of falls among the elderly population is one that may lead to dire consequences. It can significantly affect the quality of life of the victims and even lead to their premature death. Many technological tools have been proposed in the literature to detect falls, but little effort has been done regarding their prevention. In this paper, our research team proposes an inexpensive open vibration platform equipped with pressure sensors. The platform is built from easily available electronic components to be used as a tool by physiotherapists in order to help them in their evaluation of the postural control of individuals at risk of postural imbalance. The platform has been built to be easily reproducible by the scientific community. Moreover, the computer code necessary to make it work is fully open source and can be used in any non-commercial applications. A first version of the platform was tested with 7 healthy human participants. A simple reinforcement learning agent was deployed and tested to automatically calibrate the vibration motors for optimal stimulation. The agent exploited computer vision to capture the data from a force platform commercially available and use it as ground truth. Finally, a second version of the platform was built and is presented in the paper. That version is currently being validated clinically with both healthy and impaired human participants. The preliminary data are presented in this paper.
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We thank the Canadian Foundation for Innovation (CFI) whose contribution provided the necessary equipment to build the prototypes.
The authors received grants from the Fondation de l’UQAC and to the Centre Intersectoriel en Santé Durable (CISD) that enabled them to conduct this research.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Lafontaine, V., Lapointe, P., Bouchard, K. et al. An open vibration and pressure platform for fall prevention with a reinforcement learning agent. Pers Ubiquit Comput 25, 7–19 (2021). https://doi.org/10.1007/s00779-020-01416-0
- Vibration motors
- Force platform
- Postural control
- Reinforcement learning
- Technology for health