Limb motion mechanics analysis of Taijiquan trainees combined with APAS image analysis system
Analyzing the motion mechanics of Taijiquan trainees’ limbs can help trainees better understand the mechanics of Taijiquan movement and improve their training speed. However, all the current motion mechanics analysis models still have the disadvantage of poor motion mechanics analysis due to inaccurate motion image feature extraction. Therefore, this study utilizes the Ariel performance analysis system and temporal-difference to construct a motion mechanics analysis model to extract motion image features of Taijiquan and analyze its motion mechanics. analysis. The study first experimented with this analysis model. The outcomes indicated that the model achieved 100% accuracy in the extraction of image feature information and 100% accuracy in the analysis of motion mechanics, which was much higher than the comparative analysis model. The model was then used to analyze the motion mechanics of Taijiquan trainees during bending and squatting posture. The results revealed that the larger the knee fixation angle was, the shorter the completion time of the movement was. When the knee fixation angle was 150°, the time taken to complete the starting, fixing and finishing phases of the movement was 11.21 s, 61.21 s and 12.32 s respectively. Moreover, when the trainees performed the movement, the angle of the trainee’s right knee changed gently in the starting phase, while the angle of the trainee’s left knee changed more drastically. In summary, the motion mechanics analysis model proposed in the study is able to accurately analyze the limb motion mechanics of Taijiquan trainees as a means of avoiding various injuries during training.
Authors
- Yan Jin (ORCID: https://orcid.org/0000-0002-7668-4803)
- Zhaoyun Sun (ORCID: https://orcid.org/0000-0001-8753-6198)
Publication Details
- Journal
- Molecular & cellular biomechanics
- Published
- 2024-12-30
- DOI
- https://doi.org/10.62617/mcb1033
- Primary Topic
- Sports Dynamics and Biomechanics
- Type
- article
- Field-Weighted Citation Impact
- 0.00