International Journal of Medical and All Body Health Research  |  e-ISSN: 2582-8940  |  Double-Blind Peer Review  |  Open Access  |  CC BY-NC-SA 4.0

Current Issues
     2026:7/4

International Journal of Medical and All Body Health Research

e-ISSN: 2582-8940 | Open Access

Real-time Squat Pose Assessment and Injury Risk Prediction Based on Enhanced Temporal Convolutional Neural Networks

Jiaxiong Weng, Xiaoxiao Jiang, Yizhe Chen

Master of Science in Management and Systems, New York University, NY, USA; Computer Science & Engineering, Santa Clara University, CA, USA; Computer Science, University of California, San Diego, CA, USA

Article Information

Volume / Issue5 / 1 (2024)
Pages53-62
AccessOpen Access

Publication Timeline

  • Received25 December 2023
  • Accepted29 January 2024

Full Text (PDF)

Open Access - Free to Download

Download Full Article (PDF) JATS XML

Abstract

This paper presents a novel approach to real-time squat pose assessment and injury risk prediction using an enhanced temporal convolutional neural network (TCNN) architecture. The proposed system integrates multi-stream feature extraction with biomechanical constraints to achieve accurate movement analysis while maintaining real-time performance. The architecture implements parallel processing pathways optimized for spatial-temporal feature extraction, achieving a 37.8% reduction in processing latency compared to existing methods. A comprehensive dataset of 15,000 squat movement sequences from 500 participants was used to train and validate the system. The enhanced TCNN incorporates adaptive attention mechanisms and biomechanical constraints, resulting in a 44.8% decrease in mean angular error and a 27.3% improvement in physiological validity. Experimental results demonstrate real-time performance at 35.7 FPS with 94.7% assessment accuracy across diverse movement patterns. The system maintains sub-30ms latency while providing comprehensive movement analysis and risk assessment capabilities. The integration of constraint enforcement mechanisms ensures anatomically plausible pose estimates without compromising computational efficiency. The proposed approach advances the state-of-the-art in automated movement assessment, establishing a new benchmark for real-time biomechanical analysis systems in athletic training and rehabilitation applications.

Keywords

Human Pose EstimationTemporal Convolutional Neural NetworksBiomechanical AnalysisReal-time Movement Assessment

References

  1. Liu H, Ren C. An Effective 3D Human Pose Estimation Method Based on Dilated Convolutions for Videos. In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE; 2019:2327-31. [Google Scholar][Crossref]
  2. Lu N, Wu Y, Feng L, Song J. Deep learning for fall detection: Three-dimensional CNN combined with LSTM on video kinematic data. IEEE Journal of Biomedical and Health Informatics. 2018;23(1):314-23. [Google Scholar][Crossref]
  3. Rubiagatra D, Wibawa AD, Yuniarno EM. Evaluating Squat Technique in Pound Fitness through Deep Learning and Human Pose Estimations. In: 2024 7th International Conference on Informatics and Computational Sciences (ICICoS). IEEE; 2024:382-7. [Google Scholar][Crossref]
  4. Johnson WR, Alderson J, Lloyd D, Mian A. Predicting athlete ground reaction forces and moments from spatio-temporal driven CNN models. IEEE Transactions on Biomedical Engineering. 2018;66(3):689–94. [Google Scholar][Crossref]
  5. Sangeethalakshmi K, Lakshmi VV, Malathi N, Velmurugan S. Smart Ergonomic Practices With IoT and Cloud Computing for Injury Prevention and Human Motion Analysis. In: 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI). IEEE; 2023:1-6. [Google Scholar][Crossref]
  6. Hu C, Li M. Leveraging Deep Learning for Social Media Behavior Analysis to Enhance Personalized Learning Experience in Higher Education: A Case Study of Computer Science Students. Journal of Advanced Computing Systems. 2024;4(11):1-14. [Google Scholar][Crossref]
  7. Jin M, Zhou Z, Li M, Lu T. A Deep Learning-based Predictive Analytics Model for Remote Patient Monitoring and Early Intervention in Diabetes Care. International Journal of Innovative Research in Engineering and Management. 2024;11(6):80-90. [Google Scholar][Crossref]
  8. Zheng S, Li M, Bi W, Zhang Y. Real-time Detection of Abnormal Financial Transactions Using Generative Adversarial Networks: An Enterprise Application. Journal of Industrial Engineering and Applied Science. 2024;2(6):86-96. [Google Scholar][Crossref]
  9. Li L, Xiong K, Wang G, Shi J. AI-Enhanced Security for Large-Scale Kubernetes Clusters: Advanced Defense and Authentication for National Cloud Infrastructure. Journal of Theory and Practice of Engineering Science. 2024;4(12):33-47. [Google Scholar][Crossref]
  10. Ma D. Standardization of Community-Based Elderly Care Service Quality: A Multi-dimensional Assessment Model in Southern California. Journal of Advanced Computing Systems. 2024;4(12):15-27. [Google Scholar][Crossref]
  11. Zhao Q, Zhou Z, Liu Y. PALM: Personalized Attention-based Language Model for Long -tail Query Understanding in Enterprise Search Systems. Journal of AI-Powered Medical Innovations. 2024;2(1):125-40. [Google Scholar][Crossref]
  12. Yan L, Zhou S, Zheng W, Chen J. Deep Reinforcement Learning-based Resource Adaptive Scheduling for Cloud Video Conferencing Systems. [Unpublished manuscript]; c2024. [Google Scholar][Crossref]
  13. Zheng W, Zhao Q, Xie H. Research on Adaptive Noise Mechanism for Differential Privacy Optimization in Federated Learning. Journal of Knowledge Learning and Science Technology. 2024;3(4):383–92. [Google Scholar][Crossref]
  14. Yu P, Yi J, Huang T, Xu Z, Xu X. Optimization of International Journal of Medical and All Body Health Research www.allmedicaljournal.com Transformer heart disease prediction model based on particle swarm optimization algorithm. arXiv preprint. 2024;arXiv:2412.02801. [Google Scholar][Crossref]
  15. Li M, Shu M, Lu T. Anomaly Pattern Detection in High-Frequency Trading Using Graph Neural Networks. Journal of Industrial Engineering and Applied Science. 2024;2(6):77-85. [Google Scholar][Crossref]
  16. Zheng H, Xu K, Zhang M, Tan H, Li H. Efficient resource allocation in cloud computing environments using AI-driven predictive analytics. Applied and Computational Engineering. 2024;82:6-12. [Google Scholar][Crossref]
  17. Ju C, Shen Q, Ni X. Leveraging LSTM Neural Networks for Stock Price Prediction and Trading Strategy Optimization in Financial Markets. Applied and Computational Engineering. 2024;112:47-53. [Google Scholar][Crossref]
  18. Ma X, Lu T, Jin G. AI-Driven Optimization of Rare Disease Drug Supply Chains: Enhancing Efficiency and Accessibility in the US Healthcare System. Applied and Computational Engineering. 2024;99:95-102. [Google Scholar][Crossref]
  19. Ma D, Zheng W, Lu T. Machine Learning-Based Predictive Model for Service Quality Assessment and Policy Optimization in Adult Day Health Care Centers. International Journal of Innovative Research in Engineering and Management. 2024;11(6):55-67. [Google Scholar][Crossref]
  20. Ma X, Chen C, Zhang Y. Privacy-Preserving Federated Learning Framework for Cross-Border Biomedical Data Governance: A Value Chain Optimization Approach in CRO/CDMO Collaboration. Journal of Advanced Computing Systems. 2024;4(12):1-14. [Google Scholar][Crossref]

How to Cite

Weng, J., Jiang, X., & Chen, Y. (2024). Real-time Squat Pose Assessment and Injury Risk Prediction Based on Enhanced Temporal Convolutional Neural Networks. International Journal of Medical and All Body Health Research, 5(1), 53-62. https://doi.org/10.54660/IJMBHR.2024.5.1.53-62

Weng, Jiaxiong, et al. "Real-time Squat Pose Assessment and Injury Risk Prediction Based on Enhanced Temporal Convolutional Neural Networks." International Journal of Medical and All Body Health Research, vol. 5, no. 1, 2024, pp. 53-62. https://doi.org/10.54660/IJMBHR.2024.5.1.53-62.

Weng, J., Jiang, X. and Chen, Y. (2024) 'Real-time Squat Pose Assessment and Injury Risk Prediction Based on Enhanced Temporal Convolutional Neural Networks', International Journal of Medical and All Body Health Research, 5(1), pp. 53-62. doi: 10.54660/IJMBHR.2024.5.1.53-62.

Weng J, Jiang X, Chen Y. Real-time Squat Pose Assessment and Injury Risk Prediction Based on Enhanced Temporal Convolutional Neural Networks. International Journal of Medical and All Body Health Research. 2024;5(1):53-62. doi:10.54660/IJMBHR.2024.5.1.53-62

Similar Articles

Share

Find this Article on

Google Scholar Semantic Scholar OpenAlex Scilit Crossref ResearchGate BASE

A newly published article may take a few days to appear on these services.