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Sensors 2018, 18(1), 268; https://doi.org/10.3390/s18010268

A More Efficient Transportable and Scalable System for Real-Time Activities and Exercises Recognition

LIARA Laboratory, Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, Canada
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Received: 15 December 2017 / Revised: 11 January 2018 / Accepted: 12 January 2018 / Published: 18 January 2018
(This article belongs to the Special Issue Sensing, Data Analysis and Platforms for Ubiquitous Intelligence)
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Abstract

Many people in the world are affected by muscle wasting, especially the population hits by myotonic dystrophy type 1 (DM1). Those people are usually given a program of multiple physical exercises to do. While DM1 and many other people have difficulties attending commercial centers to realize their program, a solution is to develop such a program completable at home. To this end, we developed a portable system that patients could bring home. This prototype is an improved version of the previous one using Wi-Fi, as this new prototype runs on BLE technology. This new prototype conceptualized induces great results. View Full-Text
Keywords: activity recognition; wearable; power efficiency; machine learning activity recognition; wearable; power efficiency; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Chapron, K.; Plantevin, V.; Thullier, F.; Bouchard, K.; Duchesne, E.; Gaboury, S. A More Efficient Transportable and Scalable System for Real-Time Activities and Exercises Recognition. Sensors 2018, 18, 268.

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