Finger State Progress Model for Virtual Fine Motor Stroke Rehabilitation

Abstract

Manual observation in measuring and assessing stroke patient progress in fine motor rehabilitation will lead to inconsistencies especially when the patient is evaluated by different therapists or attends different rehabilitation facilities. In addition, it also increases therapist workload if they need to supervise many patients at the same time. Thus, a model was proposed to capture finger data from motion sensor device using Time-Based Simplified Denavit-Heartenberg (TS-DH) and the Finger State progress (FSP) model. Actual finger movement was compared with patterns of finger state for real-time evaluation of finger movement progress. The model will assist therapists in real-time or post-exercise evaluation of patient progress and analysis can be done during stroke rehabilitation exercise. As a conclusion, the model can be used efficiently in virtual stroke rehabilitation as real-time indicator or as a long term analysis to compare prior progress.