@article{4445,
  author = {Zhang Limei},
  title = {Analysis of Limb Motion Training and Rehabilitation Capture based on Human Motion Capture},
  journal = {Journal of Intelligent Computing},
  year = {2025},
  volume = {16},
  number = {2},
  doi = {https://doi.org/10.6025/jic/2025/16/2/47-59},
  url = {https://www.dline.info/jic/fulltext/v16n2/jicv16n2_1.pdf},
  abstract = {This article studies the rehabilitation capture analysis method for limb motion training based on human
motion capture. This method aims to improve the effectiveness of exercise training and the quality of rehabilitation
by capturing the motion characteristics of the human body and conducting training and rehabilitation
analysis on limb movements. On this basis, this article proposes a limb motion rehabilitation analysis
method based on fuzzy decision trees. This method utilizes fuzzy set theory and decision tree algorithm to
evaluate and predict the rehabilitation effect of limb movements. Specifically, a series of fuzzy rules are first
set based on experience, and then the membership degrees of each rule are calculated based on the collected
motion data. Finally, a decision tree model reflecting the rehabilitation effect is constructed using the decision
tree algorithm. The experimental results indicate that the rehabilitation capture analysis method for
limb motion training based on human motion capture can accurately evaluate and predict the rehabilitation
effect of limbs, providing strong support for exercise training and rehabilitation treatment.},
}