Development and Validation of a Wearable Intelligent Telerehabilitation Device for Postoperative Rehabilitation of
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As one of the most common sports injuries, lateral ankle sprains often lead to chronic lateral ankle instability (CLAI), which may require ankle lateral stabilization surgery to enhance ankle stability. Postoperative rehabilitation is crucial for patients to regain pre-injury sports capabilities, yet traditional rehabilitation methods are time-consuming and costly, relying heavily on subjective and objective clinical assessments. Therefore, this study has developed a wearable intelligent telerehabilitation device designed to offer cost-effective postoperative rehabilitation progress evaluations for CLAI patients. The developed device integrates a portable sensor system including pressure insoles and inertial measurement units (IMUs) to capture gait and biomechanical data, and an evaluation algorithm employing few-shot learning model to enhance model performance with small datasets. The system was trained and tested using gait data collected from 102 patients, labelled by a professional clinician with over 20 years of surgical experiences through subjective self-reported fuctions, physical evaluation, and objective examination. In the test stage, the system demonstrated an accuracy of 0.89, recall of 0.88, specificity of 0.89, and an overall F1 score of 0.90, initially fulfills the clinical requirements. Compared to traditional machine learning models, the few-shot learning approach improved accuracy by at least 0.17 and the F1-score by 0.13. The device's cost-effectiveness, ease of use, and high repeatability make it a promising tool for at-scale both clinical and home-based rehabilitation. Besides, this study creatively introduced the few-shot learning method to the field of rehabilitation, offering a solution to address the challenge of limited high-quality data in rehabilitation studies, promoting the development of intelligent healthcare.
