Enabling remote rehabilitation through human digital twins: A technological perspective

Remote rehabilitation, or telerehabilitation, has emerged as a scalable modality for delivering rehabilitative care outside clinical settings. Human Digital Twin (HDT) offers a promising paradigm for enabling personalized and adaptive telerehabilitation by continuously representing a patient's functional condition through digital technologies. Despite the growing interest in this field, current research still lacks a comprehensive understanding of how enabling technologies contribute to HDT-based telerehabilitation. This systematic review provides a technological perspective on telerehabilitation with the aim of identifying recurring architectural patterns, analyzing current technological limitations, and deriving design guidelines for future HDT-based rehabilitation systems. Following the PRISMA methodology, fifty-four studies published between 2021 and 2026 were retrieved from Scopus, ScienceDirect, and PubMed and analyzed according to six dimensions: clinical domain, type of Extended Reality (XR) technology, integration of Internet of Medical Things (IoMT) devices, Artificial Intelligence (AI) task, HDT implementation level, and study maturity level. Across the reviewed studies, a common technological workflow emerged in which sensing technologies, AI models, XR interfaces, and network infrastructures jointly support remote monitoring, rehabilitation assessment, and clinical supervision. To translate these findings into actionable recommendations, a Quality Function Deployment (QFD) analysis was conducted to map clinical and operational requirements to technological features. Based on the QFD analysis, a set of guidelines is proposed to translate the identified technological priorities into practical design principles for future research and the development of integrated, scalable, and clinically deployable telerehabilitation frameworks.