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

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 p...

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Main Authors: Kévin Chapron, Valère Plantevin, Florentin Thullier, Kévin Bouchard, Elise Duchesne, Sébastien Gaboury
Format: Article
Language:English
Published: MDPI AG 2018-01-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/18/1/268
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author Kévin Chapron
Valère Plantevin
Florentin Thullier
Kévin Bouchard
Elise Duchesne
Sébastien Gaboury
author_facet Kévin Chapron
Valère Plantevin
Florentin Thullier
Kévin Bouchard
Elise Duchesne
Sébastien Gaboury
author_sort Kévin Chapron
collection DOAJ
description 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.
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spelling doaj.art-7054047c03ec4f92be1fe11bb149df8c2022-12-22T02:10:20ZengMDPI AGSensors1424-82202018-01-0118126810.3390/s18010268s18010268A More Efficient Transportable and Scalable System for Real-Time Activities and Exercises RecognitionKévin Chapron0Valère Plantevin1Florentin Thullier2Kévin Bouchard3Elise Duchesne4Sébastien Gaboury5LIARA Laboratory, Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, CanadaLIARA Laboratory, Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, CanadaLIARA Laboratory, Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, CanadaLIARA Laboratory, Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, CanadaLIARA Laboratory, Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, CanadaLIARA Laboratory, Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, CanadaMany 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.http://www.mdpi.com/1424-8220/18/1/268activity recognitionwearablepower efficiencymachine learning
spellingShingle Kévin Chapron
Valère Plantevin
Florentin Thullier
Kévin Bouchard
Elise Duchesne
Sébastien Gaboury
A More Efficient Transportable and Scalable System for Real-Time Activities and Exercises Recognition
Sensors
activity recognition
wearable
power efficiency
machine learning
title A More Efficient Transportable and Scalable System for Real-Time Activities and Exercises Recognition
title_full A More Efficient Transportable and Scalable System for Real-Time Activities and Exercises Recognition
title_fullStr A More Efficient Transportable and Scalable System for Real-Time Activities and Exercises Recognition
title_full_unstemmed A More Efficient Transportable and Scalable System for Real-Time Activities and Exercises Recognition
title_short A More Efficient Transportable and Scalable System for Real-Time Activities and Exercises Recognition
title_sort more efficient transportable and scalable system for real time activities and exercises recognition
topic activity recognition
wearable
power efficiency
machine learning
url http://www.mdpi.com/1424-8220/18/1/268
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