System for Detection and Quantitative Evaluation of Compensatory Movement in Post-Stroke Patients Based on Wearable Sensor and Machine Learning Algorithm

During upper limb rehabilitation training and assessment, compensatory movements can significantly impede the recovery of motor function in stroke survivors. Current methods for detecting these movements primarily rely on visual sensors, torso restraint belts, and pressure pads. However, these approaches present challenges such as obstacle occlusion, complex operation, and an inability to provide a quantitative assessment of compensatory movements. To address these issues, we developed a system for detecting and quantitatively evaluating compensatory movements, utilizing wearable sensors and machine learning algorithms. We recruited 30 healthy volunteers and 32 stroke patients to complete seven experimental motions. Their movement data were recorded using three inertial measurement units (IMUs) placed on the upper limbs and sternum. After preprocessing the data, six machine learning algorithms [K-nearest neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP), random forest (RF), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)] were employed to train a multilabel classification model for detecting and quantitatively evaluating compensatory movements. The algorithm with the highest performance was selected as the system’s classifier. The results show that the proposed system improves the number and accuracy of compensatory movement detection and achieves accurate classification and detection of seven compensatory movements (average accuracy is 95.7%, F1-score is 96.8%). Furthermore, the KNN classifier classified compensatory movements as mild, moderate, and severe according to the different severity of the compensatory movements and obtained an average accuracy of 93.7%, which proved the reliability and responsiveness of the system. This system is applicable to stroke patients in clinical, home, and community-based rehabilitation settings, enabling a quantitative assessment of compensatory movements.