Automatic rehabilitation assessment method of upper limb motor function based on posture and distribution force - Bai, Jing; Li, Guocheng; Lu, Xuanming; Wen, Xiulan (Frontiers in Neuroscience, 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. Additionally, an interactive interface incorporating instructional videos, virtual feedback scenarios and distributed force results was also designed. 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 (r=0.99, p<0.001). This indicates that the system can accurately measure upper extremity motion and stiffness in patients. The comparison of the characteristics of the affected limb and the unaffected limb can serve as the basis for standardizing rehabilitation assessment systems. provides a scientific basis for formulating rehabilitation treatment programs, evaluating patients' functional changes, and judging treatment effects and prognosis.