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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10222347 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT coateswilliam leanrealtimeanalysisofresistancetrainingusingwearablecomputing AT wahlstromjohan leanrealtimeanalysisofresistancetrainingusingwearablecomputing |