Abstract
The growing demand for personalized and effective rehabilitation strategies has driven the development of AI-driven smart wearable systems. These systems provide real-time health monitoring, prediction, and adaptive feedback, which helps to improve clinical outcomes in neuro-musculoskeletal and postoperative rehabilitation. This scoping review analyzes 14 peer-reviewed articles published between 2018 and 2025, comprising clinical trials, cohort studies, and engineering applications. The selected studies were identified through structured searches in IEEE, MDPI, and other scholarly databases, based on relevance to AI-enhanced wearable rehabilitation devices. Commonly used AI algorithms include support vector machines (SVM), convolutional neural networks (CNN), and reinforcement learning (RL), enabling functions such as gait analysis, joint movement recognition, muscle activation tracking, and postural control. In addition, integrationwith IoT sensor networks, cloud-based platforms, and telemedicine interfaces was widely reported. The review finds that AI-enabled wearables significantly improve patient adherence, monitoring accuracy, and personalized therapy delivery. Nonetheless, challenges remain in data security, sensor calibration, interoperability, and long-term user retention. These results confirm that smart wearables play an important role in supporting personalized, data-driven rehabilitation.Keywords: AI wearables, personalized rehabilitation, remote monitoring, gait analysis, machine learning, telemedicine, smart health.