Monitoring Rehabilitation of Stroke Patients Using Automated Fugl-Meyer Assessment

Abstract: The Fugl-Meyer Assessment (FMA) is a widely used method for evaluating the motor function of stroke patients. During the assessment, patients are instructed to perform a series of predefined motions outlined in the FMA manual, while an evaluator scores each motion using a 3-point Likert scale. A score of 2 is given for flawless execution, a score of 1 for partial completion, and a score of 0 for no execution or lack of motion. Traditional FMA assessments rely on manual visual inspection, but recent studies have explored automating the process using motion capture technology or IMU and EMG sensors. However, motion capture technology is limited in terms of portability due to its complex setup, making IMU and EMG sensors the preferred choice. Previous studies utilizing these sensors have collected data to train machine learning algorithms for predicting FMA scores. Although this approach eliminates manual inspection, it still yields a 3-point score based on the Likert scale, which can be ambiguous and fails to capture subtle improvements or differences in motor function during motion execution. To address this issue, the current study aims to implement a Modified Automated FMA that employs a percentage-based scoring system to overcome the ambiguity of the 3-point Likert scale. Scoring will be based on data collected from IMU and EMG sensors while participants perform various upper limb motions. A maximum threshold will be established as the baseline, representing the normal range of motion for individuals without mobility impairment. The assumption is that stroke patients with mobility impairments will struggle to achieve the normal range of motion indicated in the FMA manual, resulting in sensor data falling below the maximum threshold. The dataset will be obtained by instructing participants to perform a series of upper limb motions. Two scenarios will be simulated to train and test the algorithm. The first scenario involves full execution of the upper limb motions, serving as baseline data for the normal range of motion. The second scenario entails partial execution of the motions, representing data from individuals with mobility impairment. By training the algorithm on this dataset using Support Vector Machine (SVM) and Dynamic Time Warping (DTW), it will be capable of detecting whether a participant's motion falls within the normal range and providing immediate feedback in the form of percentage scores through, indicating the deviation from normal execution. In conclusion, the Modified Automated FMA, utilizing a percentage-based scoring system based on IMU and EMG sensors, offers a promising solution for assessing motor function in stroke patients. The percentage-based scoring system provides a precise assessment of motor function, capturing even subtle improvements which contribute to improved treatment planning and better tracking of rehabilitation outcomes. Additionally, the integration of digital twin technology with wearable devices allows for remote rehabilitation and personalized care. Patients can now engage in rehabilitation exercises from the comfort of their homes while healthcare professionals remotely monitor their progress. This innovative approach enhances the quality of life for stroke patients by providing convenient access to rehabilitation services and personalized feedback.

Keywords: Fugl-Meyer Assessment, Rehabilitation, Stroke, IMU, EMG, SVM, DTW

DOI: 10.54941/ahfe1005059

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