Development and Iterative Optimization of a Sensor-Based Hand Function Assessment Tool for Stroke Rehabilitation

Stroke rehabilitation requires objective, quantifiable assessments of motor function to track progress and guide interventions. Current clinical scales rely on subjective, time-consuming and clinically trained observation, limiting widespread adoption. This study presents a compact, sensor-based system for precise, fast, and automated measurement of key motor metrics of the hand—Grip Force, Pinch Force, Motor Speed, Manual Dexterity, and Finger Flexion—identified through literature review and clinician inputs. The system integrates load cells, a custom automated goniometer, force-sensitive resistors, and infrared sensors for accurate measurement. A state-based Arduino interface processes and transmits data to a web application built in-house for real-time visualization. Bench testing ensured measurement reliability, followed by usability testing with five post-stroke patients to assess ease of use, accuracy, and clinical relevance. Results showed significant deviations of patient performance from normative values, particularly in pinch strength and fine motor tasks, confirming the system's sensitivity to impairment. Design refinements improved sensor placement and measurement accuracy. Patient and clinician feedback highlighted high usability and meaningful insights into recovery. This low-cost tool bridges the gap between clinical expertise and accessible yet objective assessment. By enabling precise, real-time monitoring of motor function, it has the potential to transform stroke rehabilitation by making standardized assessments more accessible, data-driven, and clinically actionable.