融合多肌肉形态特征的下肢肌肉疲劳因子分析方法、系统、存储介质
By integrating the synergistic morphological changes of the vastus lateralis and vastus medialis muscles using a three-dimensional frustum volume model and an interpretable statistical model, the problem of insufficient multi-muscle synergistic analysis and quantitative indicators in lower limb muscle fatigue monitoring is solved. This enables the quantification of fatigue level and explanation of its causes, and is applicable to human-computer interaction and sports rehabilitation training.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SUZHOU UNIV
- Filing Date
- 2026-05-07
- Publication Date
- 2026-07-17
AI Technical Summary
Current technologies for monitoring lower limb muscle fatigue lack multi-muscle synergistic analysis and reliable quantitative indicators, making it impossible to effectively quantify the degree and causes of fatigue and thus failing to support the need for precise intervention.
By integrating the synergistic morphological changes of the vastus lateralis and vastus medialis muscles using a three-dimensional frustum volume model and combining it with an interpretable statistical model, the system outputs quantitative indicators of fatigue level and the contribution of fatigue causes. Motion capture markers are used to obtain muscle morphological features, and a multidimensional feature vector is constructed. The fatigue state is then quantified through analysis of variance and principal component analysis.
It enables the explanation and quantification of the causes of lower limb muscle fatigue, provides a basis for muscle fatigue assessment, and is suitable for real-time monitoring in human-computer interaction and sports rehabilitation training.
Smart Images
Figure CN122153809B_ABST