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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Bibliographic Details
Published in:Information (Basel) 2023-07, Vol.14 (7), p.402
Main Authors: Arciniega-Rocha, Ricardo P, Erazo-Chamorro, Vanessa C, Rosero-Montalvo, Paúl D, Szabó, Gyula
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Annotations
Athletes
back injuries
Coaches & managers
Data collection
Decision making
Deep learning
embedded systems
ergonomics risk
Human subjects
Injuries
Injury prevention
intelligent systems
Kinematics
Knee
lifting loads
Machine learning
Microcontrollers
Posture
Sensors
Sports injuries
squat analysis
Training
Wearable technology
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Summary: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.
ISSN:2078-2489
2078-2489
DOI:10.3390/info14070402