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LEAN: Real-Time Analysis of Resistance Training Using Wearable Computing

The use of fitness apps to track physical exercise has been proven to promote weight loss and increase physical activity. The most popular forms of exercise are cardiovascular training and resistance training. The overwhelming majority of cardio tracking apps automatically track and analyse outdoor...

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Detalles Bibliográficos
Autores principales: Coates, William, Wahlström, Johan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10222347/
https://www.ncbi.nlm.nih.gov/pubmed/37430515
http://dx.doi.org/10.3390/s23104602
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author Coates, William
Wahlström, Johan
author_facet Coates, William
Wahlström, Johan
author_sort Coates, William
collection PubMed
description The use of fitness apps to track physical exercise has been proven to promote weight loss and increase physical activity. The most popular forms of exercise are cardiovascular training and resistance training. The overwhelming majority of cardio tracking apps automatically track and analyse outdoor activity with relative ease. In contrast, nearly all commercially available resistance tracking apps only record trivial data, such as the exercise weight and repetition number via manual user input, a level of functionality not far from that of a pen and paper. This paper presents LEAN, a resistance training app and exercise analysis (EA) system for both the iPhone and Apple Watch. The app provides form analysis using machine learning, automatic repetition counting in real time, and other important but seldom studied exercise metrics, such as range of motion on a per-repetition level and average repetition time. All features are implemented using lightweight inference methods that enable real-time feedback on resource-constrained devices. The performance evaluation includes a user survey and benchmarking of all data science features using both ground-truth data from complementary modalities and comparisons with commercial apps.
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spelling pubmed-102223472023-05-28 LEAN: Real-Time Analysis of Resistance Training Using Wearable Computing Coates, William Wahlström, Johan Sensors (Basel) Article The use of fitness apps to track physical exercise has been proven to promote weight loss and increase physical activity. The most popular forms of exercise are cardiovascular training and resistance training. The overwhelming majority of cardio tracking apps automatically track and analyse outdoor activity with relative ease. In contrast, nearly all commercially available resistance tracking apps only record trivial data, such as the exercise weight and repetition number via manual user input, a level of functionality not far from that of a pen and paper. This paper presents LEAN, a resistance training app and exercise analysis (EA) system for both the iPhone and Apple Watch. The app provides form analysis using machine learning, automatic repetition counting in real time, and other important but seldom studied exercise metrics, such as range of motion on a per-repetition level and average repetition time. All features are implemented using lightweight inference methods that enable real-time feedback on resource-constrained devices. The performance evaluation includes a user survey and benchmarking of all data science features using both ground-truth data from complementary modalities and comparisons with commercial apps. MDPI 2023-05-09 /pmc/articles/PMC10222347/ /pubmed/37430515 http://dx.doi.org/10.3390/s23104602 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Coates, William
Wahlström, Johan
LEAN: Real-Time Analysis of Resistance Training Using Wearable Computing
title LEAN: Real-Time Analysis of Resistance Training Using Wearable Computing
title_full LEAN: Real-Time Analysis of Resistance Training Using Wearable Computing
title_fullStr LEAN: Real-Time Analysis of Resistance Training Using Wearable Computing
title_full_unstemmed LEAN: Real-Time Analysis of Resistance Training Using Wearable Computing
title_short LEAN: Real-Time Analysis of Resistance Training Using Wearable Computing
title_sort lean: real-time analysis of resistance training using wearable computing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10222347/
https://www.ncbi.nlm.nih.gov/pubmed/37430515
http://dx.doi.org/10.3390/s23104602
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