Bai Jing, Li Guocheng, Lu Xuanming, Wen Xiulan
Industrial Technology Research Institute of Intelligent Equipment, Nanjing Institute of Technology, Nanjing, China.
Jiangsu Provincial Engineering Laboratory of Intelligent Manufacturing Equipment, Nanjing, China.
Front Neurosci. 2024 Feb 19;18:1362495. doi: 10.3389/fnins.2024.1362495. eCollection 2024.
The clinical rehabilitation assessment methods for hemiplegic upper limb motor function are often subjective, time-consuming, and non-uniform. This study proposes an automatic rehabilitation assessment method for upper limb motor function based on posture and distributed force measurements. Azure Kinect combined with MediaPipe was used to detect upper limb and hand movements, and the array distributed flexible thin film pressure sensor was employed to measure the distributed force of hand. This allowed for the automated measurement of 30 items within the Fugl-Meyer scale. Feature information was extracted separately from the affected and healthy sides, the feature ratios or deviation were then fed into a single/multiple fuzzy logic assessment model to determine the assessment score of each item. Finally, the total score of the hemiplegic upper limb motor function assessment was derived. Experiments were performed to evaluate the motor function of the subjects' upper extremities. Bland-Altman plots of physician and system scores showed good agreement. The results of the automated assessment system were highly correlated with the clinical Fugl-Meyer total score ( = 0.99, < 0.001). The experimental results state that this system can automatically assess the motor function of the affected upper limb by measuring the posture and force distribution.
偏瘫上肢运动功能的临床康复评估方法往往具有主观性、耗时且不统一。本研究提出了一种基于姿势和分布力测量的上肢运动功能自动康复评估方法。使用Azure Kinect结合MediaPipe来检测上肢和手部运动，并采用阵列分布式柔性薄膜压力传感器来测量手部的分布力。这使得能够自动测量Fugl-Meyer量表中的30项内容。分别从患侧和健侧提取特征信息，然后将特征比率或偏差输入单/多模糊逻辑评估模型，以确定各项的评估分数。最后得出偏瘫上肢运动功能评估的总分。进行实验以评估受试者上肢的运动功能。医生评分与系统评分的Bland-Altman图显示出良好的一致性。自动评估系统的结果与临床Fugl-Meyer总分高度相关（=0.99，<0.001）。实验结果表明，该系统可以通过测量姿势和力分布来自动评估患侧上肢的运动功能。
