Smart Wearable to Prevent Injuries in Amateur Athletes in Squats Exercise by Using Lightweight Machine Learning Model

An erroneous squat movement might cause different injuries in amateur athletes who are not experts in workout exercises. Even when personal trainers watch out for the athletes’ workout performance, light variations in ankles, knees, and lower back movements might not be recognized. Therefore, we pre...
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Autor*in:	Ricardo P. Arciniega-Rocha [verfasserIn] Vanessa C. Erazo-Chamorro [verfasserIn] Paúl D. Rosero-Montalvo [verfasserIn] Gyula Szabó [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	squat analysis lifting loads ergonomics risk intelligent systems back injuries embedded systems

Übergeordnetes Werk:	In: Information - MDPI AG, 2010, 14(2023), 7, p 402
Übergeordnetes Werk:	volume:14 ; year:2023 ; number:7, p 402

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/info14070402

Katalog-ID:	DOAJ093887698

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callnumber-first	T - Technology
author	Ricardo P. Arciniega-Rocha
spellingShingle	Ricardo P. Arciniega-Rocha misc T58.5-58.64 misc squat analysis misc lifting loads misc ergonomics risk misc intelligent systems misc back injuries misc embedded systems misc Information technology Smart Wearable to Prevent Injuries in Amateur Athletes in Squats Exercise by Using Lightweight Machine Learning Model
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topic_title	T58.5-58.64 Smart Wearable to Prevent Injuries in Amateur Athletes in Squats Exercise by Using Lightweight Machine Learning Model squat analysis lifting loads ergonomics risk intelligent systems back injuries embedded systems
topic	misc T58.5-58.64 misc squat analysis misc lifting loads misc ergonomics risk misc intelligent systems misc back injuries misc embedded systems misc Information technology
topic_unstemmed	misc T58.5-58.64 misc squat analysis misc lifting loads misc ergonomics risk misc intelligent systems misc back injuries misc embedded systems misc Information technology
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title	Smart Wearable to Prevent Injuries in Amateur Athletes in Squats Exercise by Using Lightweight Machine Learning Model
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title_full	Smart Wearable to Prevent Injuries in Amateur Athletes in Squats Exercise by Using Lightweight Machine Learning Model
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author-letter	Ricardo P. Arciniega-Rocha
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title_sort	smart wearable to prevent injuries in amateur athletes in squats exercise by using lightweight machine learning model
callnumber	T58.5-58.64
title_auth	Smart Wearable to Prevent Injuries in Amateur Athletes in Squats Exercise by Using Lightweight Machine Learning Model
abstract	An erroneous squat movement might cause different injuries in amateur athletes who are not experts in workout exercises. Even when personal trainers watch out for the athletes’ workout performance, light variations in ankles, knees, and lower back movements might not be recognized. Therefore, we present a smart wearable to alert athletes whether their squats performance is correct. We collect data from people experienced with workout exercises and from learners, supervising personal trainers in annotation of data. Then, we use data preprocessing techniques to reduce noisy samples and train Machine Learning models with a small memory footprint to be exported to microcontrollers to classify squats’ movements. As a result, the k-Nearest Neighbors algorithm with k = 5 achieves an 85% performance and weight of 40 KB of RAM.
abstractGer	An erroneous squat movement might cause different injuries in amateur athletes who are not experts in workout exercises. Even when personal trainers watch out for the athletes’ workout performance, light variations in ankles, knees, and lower back movements might not be recognized. Therefore, we present a smart wearable to alert athletes whether their squats performance is correct. We collect data from people experienced with workout exercises and from learners, supervising personal trainers in annotation of data. Then, we use data preprocessing techniques to reduce noisy samples and train Machine Learning models with a small memory footprint to be exported to microcontrollers to classify squats’ movements. As a result, the k-Nearest Neighbors algorithm with k = 5 achieves an 85% performance and weight of 40 KB of RAM.
abstract_unstemmed	An erroneous squat movement might cause different injuries in amateur athletes who are not experts in workout exercises. Even when personal trainers watch out for the athletes’ workout performance, light variations in ankles, knees, and lower back movements might not be recognized. Therefore, we present a smart wearable to alert athletes whether their squats performance is correct. We collect data from people experienced with workout exercises and from learners, supervising personal trainers in annotation of data. Then, we use data preprocessing techniques to reduce noisy samples and train Machine Learning models with a small memory footprint to be exported to microcontrollers to classify squats’ movements. As a result, the k-Nearest Neighbors algorithm with k = 5 achieves an 85% performance and weight of 40 KB of RAM.
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title_short	Smart Wearable to Prevent Injuries in Amateur Athletes in Squats Exercise by Using Lightweight Machine Learning Model
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