Parween, GAl-Anbuky, AMawston, GLowe, A2025-11-252025-11-252025-11-01Sensors, ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 25(21), 6674-. doi: 10.3390/s252166741424-82201424-8220http://hdl.handle.net/10292/20208Prehabilitation programs for abdominal pre-operative patients are increasingly recognized for improving surgical outcomes, reducing post-operative complications, and enhancing recovery. Internet of Things (IoT)-enabled human movement monitoring systems offer promising support in mixed-mode settings that combine clinical supervision with home-based independence. These systems enhance accessibility, reduce pressure on healthcare infrastructure, and address geographical isolation. However, current implementations often lack personalized movement analysis, adaptive intervention mechanisms, and real-time clinical integration, frequently requiring manual oversight and limiting functional outcomes. This review-based paper proposes a conceptual framework informed by the existing literature, integrating Digital Twin (DT) technology, and machine learning/Artificial Intelligence (ML/AI) to enhance IoT-based mixed-mode prehabilitation programs. The framework employs inertial sensors embedded in wearable devices and smartphones to continuously collect movement data during prehabilitation exercises for pre-operative patients. These data are processed at the edge or in the cloud. Advanced ML/AI algorithms classify activity types and intensities with high precision, overcoming limitations of traditional Fast Fourier Transform (FFT)-based recognition methods, such as frequency overlap and amplitude distortion. The Digital Twin continuously monitors IoT behavior and provides timely interventions to fine-tune personalized patient monitoring. It simulates patient-specific movement profiles and supports dynamic, automated adjustments based on real-time analysis. This facilitates adaptive interventions and fosters bidirectional communication between patients and clinicians, enabling dynamic and remote supervision. By combining IoT, Digital Twin, and ML/AI technologies, the proposed framework offers a novel, scalable approach to personalized pre-operative care, addressing current limitations and enhancing outcomes.© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).IoTartificial intelligencedigital twinhuman movement monitoringprehabilitationwearable sensors4605 Data Management and Data Science46 Information and Computing SciencesNetworking and Information Technology R&D (NITRD)BioengineeringBehavioral and Social ScienceMachine Learning and Artificial IntelligenceClinical ResearchTelehealthGeneric health relevance3 Good Health and Well Being0301 Analytical Chemistry0502 Environmental Science and Management0602 Ecology0805 Distributed Computing0906 Electrical and Electronic EngineeringAnalytical Chemistry3103 Ecology4008 Electrical engineering4009 Electronics, sensors and digital hardware4104 Environmental management4606 Distributed computing and systems softwareHumansMovementInternet of ThingsMonitoring, PhysiologicWearable Electronic DevicesMachine LearningArtificial IntelligenceAlgorithmsSmartphoneHumansMonitoring, PhysiologicMovementAlgorithmsArtificial IntelligenceMachine LearningSmartphoneWearable Electronic DevicesInternet of ThingsJournal ArticleOpenAccess10.3390/s25216674