The Global Importance of Machine Learning-Based Wearables and Digital Twins for Rehabilitation: A Review of Data Collection, Security, Edge Intelligence, Federated Learning, and Generative AI

Maciej Piechowiak, Aleksander Goch, Ewelina Panas, Jolanta Masiak, Dariusz Mikołajewski, Izabela Rojek, Emilia Mikołajewska

The convergence of wearable technologies and digital twin (DT) systems is transforming rehabilitation engineering, enabling continuous monitoring, personalized therapeutic interventions, and predictive modeling of patient recovery pathways. This review examines the growing role of machine learning (ML) in the development and integration of DTs frameworks in rehabilitation, with a focus on wearable sensor data, security and privacy, edge computing architectures, federated learning paradigms, and generative artificial intelligence (GenAI) applications. We first analyze data collection processes, emphasizing multimodal sensing, signal processing, and real-time synchronization between physical and virtual patient models. We then discuss key challenges related to data security, encryption, and privacy protection, especially in distributed clinical environments. The review then assesses the role of edge computing in reducing latency, improving energy efficiency, and enabling real-time local intelligence feedback in wearable devices. Federated learning approaches are discussed as promising strategies for jointly training ML models without compromising sensitive medical data. Finally, we present new GenAI techniques for generating synthetic data, personalizing digital twins, and simulating rehabilitation scenarios. By mapping current progress and identifying research gaps, this article provides a unified view that connects electronic and biomedical engineering with intelligent, secure, and adaptive DT ecosystems for next-generation rehabilitation solutions. Wearable devices with ML and DTs for rehabilitation are developing rapidly, but their current effectiveness still depends on consistent, high-quality data streams and robust clinical validation. The most promising convergence involves combining edge intelligence with federated learning to enable real-time personalization while preserving patient privacy. GenAI further enhances these systems by simulating patient-specific scenarios, accelerating model adaptation, and treatment planning. Key challenges remain related to standardizing data formats, ensuring comprehensive security, and seamlessly integrating these technologies into clinical processes.