{"title":"Enhancing squat movement classification performance with a gated long-short term memory with transformer network model.","authors":"Xinyao Hu, Wenyue Zhang, Haopeng Ou, Shiwei Mo, Fenjie Liang, Junshi Liu, Zhong Zhao, Xingda Qu","doi":"10.1080/14763141.2024.2315243","DOIUrl":null,"url":null,"abstract":"<p><p>Bodyweight squat is one of the basic sports training exercises. Automatic classification of aberrant squat movements can guide safe and effective bodyweight squat exercise in sports training. This study presents a novel gated long-short term memory with transformer network (GLTN) model for the classification of bodyweight squat movements. Twenty-two healthy young male participants were involved in an experimental study, where they were instructed to perform bodyweight squat in nine different movement patterns, including one acceptable movement defined according to the National Strength and Conditioning Association and eight aberrant movements. Data were acquired from four customised inertial measurement units placed at the thorax, waist, right thigh, and right shank, with a sampling frequency of 200 Hz. The results show that compared to state-of-art deep learning models, our model enhances squat movement classification performance with 96.34% accuracy, 96.31% precision, 96.45% recall, and 96.32% F-score. The proposed model provides a feasible wearable solution to monitoring aberrant squat movements that can facilitate performance and injury risk assessment during sports training. However, this model should not serve as a one-size-fits-all solution, and coaches and practitioners should consider individual's specific needs and training goals when using it.</p>","PeriodicalId":49482,"journal":{"name":"Sports Biomechanics","volume":" ","pages":"1-16"},"PeriodicalIF":2.0000,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sports Biomechanics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/14763141.2024.2315243","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Bodyweight squat is one of the basic sports training exercises. Automatic classification of aberrant squat movements can guide safe and effective bodyweight squat exercise in sports training. This study presents a novel gated long-short term memory with transformer network (GLTN) model for the classification of bodyweight squat movements. Twenty-two healthy young male participants were involved in an experimental study, where they were instructed to perform bodyweight squat in nine different movement patterns, including one acceptable movement defined according to the National Strength and Conditioning Association and eight aberrant movements. Data were acquired from four customised inertial measurement units placed at the thorax, waist, right thigh, and right shank, with a sampling frequency of 200 Hz. The results show that compared to state-of-art deep learning models, our model enhances squat movement classification performance with 96.34% accuracy, 96.31% precision, 96.45% recall, and 96.32% F-score. The proposed model provides a feasible wearable solution to monitoring aberrant squat movements that can facilitate performance and injury risk assessment during sports training. However, this model should not serve as a one-size-fits-all solution, and coaches and practitioners should consider individual's specific needs and training goals when using it.
期刊介绍:
Sports Biomechanics is the Thomson Reuters listed scientific journal of the International Society of Biomechanics in Sports (ISBS). The journal sets out to generate knowledge to improve human performance and reduce the incidence of injury, and to communicate this knowledge to scientists, coaches, clinicians, teachers, and participants. The target performance realms include not only the conventional areas of sports and exercise, but also fundamental motor skills and other highly specialized human movements such as dance (both sport and artistic).
Sports Biomechanics is unique in its emphasis on a broad biomechanical spectrum of human performance including, but not limited to, technique, skill acquisition, training, strength and conditioning, exercise, coaching, teaching, equipment, modeling and simulation, measurement, and injury prevention and rehabilitation. As well as maintaining scientific rigour, there is a strong editorial emphasis on ''reader friendliness''. By emphasising the practical implications and applications of research, the journal seeks to benefit practitioners directly.
Sports Biomechanics publishes papers in four sections: Original Research, Reviews, Teaching, and Methods and Theoretical Perspectives.