Evaluating upper limb functions based on motion analysis

Conventional evaluation indices for upper limb function rehabilitation are based on the time to complete a task and the duration of movement. How-ever, these metrics are insufficient to quantify motor performance attributes, such as smoothness of movement and presence of compensatory movements. This study aims to introduce a quantitative index for the evaluation of upper limb functions based on rehabilitation exercises performed by patients. For our initial evaluation, we chose the Grasp movement performed in ARAT (Action Research Arm Test), a conventional evaluation method for upper limb functions in patients with post-stroke syndrome. We use RGB videos of therapist imitating a patient with posterior syndrome. Machine learning techniques were employed to esti-mate posture and extract skeletal information, using time-series analysis, an evaluation model was created to quantify the compensatory movements of post-stroke syndrome and healthy patients.