Demo: Myotrainer: Muscle-Aware Motion Analysis and Feedback System for In-Home Resistance Training

Resistance training is widely incorporated in exercise programs, including in-home fitness and rehabilitation. However, improper motion patterns and muscle stimulation can undermine the safety of the subjects, making precise monitoring essential. Existing solutions primarily focus on correcting motion patterns with difficulties assessing muscle contraction levels. In this work, we introduce MyoTrainer, which provides muscle-aware motion descriptions and personalized feedback in natural language. Taking a person's exercise video as input, MyoTrainer first utilizes pose estimation models to capture motion sequences in real-time. A GCN-Former model has been developed for fine-grained motion analysis, which includes action recognition, incorrect movement pattern detection, and muscle contraction intensity estimation. Additionally, MyoTrainer integrates fitness and physiotherapeutic domain knowledge to deliver personalized, professional feedback. Extensive evaluations show that our system outperforms existing solutions in all recognition tasks and a survey indicates 88.9% of users find the generated feedback to be beneficial.

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Publication Details

Published
2024-11-04
DOI
https://doi.org/10.1145/3666025.3699397
Citations
1
Primary Topic
Cardiovascular and exercise physiology
Type
article
Field-Weighted Citation Impact
0.72
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article

Demo: Myotrainer: Muscle-Aware Motion Analysis and Feedback System for In-Home Resistance Training

Doris Sau Fung Yu, Di Duan, Mu Yuan, Guoliang Xing et al.
1 citations
Cardiovascular and exercise physiology
0.72
article

Demo: Myotrainer: Muscle-Aware Motion Analysis and Feedback System for In-Home Resistance Training

Doris Sau Fung Yu, Di Duan, Mu Yuan, Guoliang Xing, Hongkai Chen, Xinyan Wang, Yuting He
article en
1 citations

Abstract

Resistance training is widely incorporated in exercise programs, including in-home fitness and rehabilitation. However, improper motion patterns and muscle stimulation can undermine the safety of the subjects, making precise monitoring essential. Existing solutions primarily focus on correcting motion patterns with difficulties assessing muscle contraction levels. In this work, we introduce MyoTrainer, which provides muscle-aware motion descriptions and personalized feedback in natural language. Taking a person's exercise video as input, MyoTrainer first utilizes pose estimation models to capture motion sequences in real-time. A GCN-Former model has been developed for fine-grained motion analysis, which includes action recognition, incorrect movement pattern detection, and muscle contraction intensity estimation. Additionally, MyoTrainer integrates fitness and physiotherapeutic domain knowledge to deliver personalized, professional feedback. Extensive evaluations show that our system outperforms existing solutions in all recognition tasks and a survey indicates 88.9% of users find the generated feedback to be beneficial.

Chinese University of Hong Kong (HK), University of Hong Kong (HK)
Openalex Percentile: Top 22%
Cardiovascular and exercise physiology
0.72
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