Novel evaluation of upper-limb motor performance after stroke based on normal reaching movement model

Automatic rehabilitation assessment method of upper limb motor function based on posture and distribution force

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 (r = 0.99, p < 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.

Research Status and Prospect of Finger Rehabilitation Machinery

About 80% of stroke patients have hand motor dysfunction, and wearing finger rehabilitation machinery can enable patients to carry out efficient passive rehabilitation training independently. At present, many typical finger rehabilitation machines have been developed, and clinical experiments have confirmed the effectiveness of mechanically assisted finger rehabilitation. In this paper, the finger rehabilitation machinery will be classified in the actuation mode, and the terminal traction drive/motor drive/spring drive/rope drive/memory alloy drive/electroactive material drive/hydraulic drive/pneumatic drive technology and its typical applications are analyzed. Study the structure, control methods, overlap between mechanical bending nodes and finger joints, training modes, response speed, and driving force of various types of finger rehabilitation machinery. The advantages and disadvantages of various actuation methods of finger rehabilitation machinery are summarized. Finally, the difficulties and opportunities faced by the future development of finger rehabilitation machinery are prospected. In general, with the continuous improvement of quality of life, stroke patients need flexible, segmented control, accurate bending, multi-training mode, fast response, and good driving force finger rehabilitation machinery. This will also be a future hot research direction.