Novel Digital Assessment System for Upper-Limb Movement in Stroke Patients Using Markless-Sensing Technology and Deep Learning Algorithms

Traditional motor function assessment for stroke patients involves subjective scoring by rehabilitation physicians, a process that is time-consuming, expensive, and subject to variability. By utilizing sensors (markers) and machine learning algorithms, digital assessment systems offer the potential for more objective clinical decision support. Nevertheless, these algorithms often rely on feature extraction, which opens up opportunities for improving diagnostic accuracy and reliability. Therefore, this study proposed a novel assessment approach based on markless-sensing technology and deep learning algorithms, which can perform contactless data measurement and nonfeature-based digital assessment. Specifically, the movement data of stroke patients were collected via the Microsoft Kinect V2 with a customized motion tracking system. The raw dataset was then processed by the Savitzky-Golay filter and long short-term memory-attention-based assessment model. A total of 25 volunteers (15 stroke patients and 10 healthy subjects) were recruited for experimental validation by conducting the commonly used clinical scale (wolf motor function test-functional ability scores, WMFT-FAS). The experiments showed that the proposed novel digital assessment system could achieve favorable results: an average accuracy of 91.7%, average precision of 87.4%, average recall of 87.3%, and average F1-score of 87.3%. In summary, the proposed system can provide objective and reasonably accurate assessment outcomes, which might have the potential to offer essential clinical information for rehabilitation physicians to make reasonable clinical intervention plans.