Digital Twin Prospects in IoT-Based Human Movement Monitoring Model
Gulfeshan Parween, Adnan Al‐Anbuky, Grant A. Mawston, Andrew D. Lowe · Preprints.org · 2025
Innovative IoT-enabled human movement monitoring systems have shown significant potential to support the prehabilitation programs in mixed-mode settings for abdominal pre-operative patients by enabling patient’s movement tracking and the performance measurement. This IoT-based prehabilitation program in mixed-mode settings, can enable clinical supervision with home-based independence with remote monitoring and enhance accessibility which alleviates pressure on healthcare resources and overcome geographical isolation. However, this existing programs often lack personalized analysis of movement dynamics and automated adaptive interventions, which can limit their effectiveness in improving functional outcomes and patient adherence. Recent research study suggests that Digital Twin technology along with IoT and ML/AI can address these limitations by enabling dynamic, adaptive, and personalized prehabilitation programs. This paper reviews the literature to investigate the efficacy of technology integration in prehabilitation programs for abdominal preoperative patients, key components and functionalities of IoT systems, and potential software capabilities of Digital Twin and AI technologies to design a conceptual framework for mixed-mode prehabilitation program. The proposed framework will collects continuous movement data on exercises involved in physical activity of prehabilitation program from inertial sensors embedded in wearable watch and smart mobile phones. Machine learning algorithms can analyze these data to identify activity type and intensity precisely, overcoming challenges such as overlapping of dominant frequencies and amplitudes that occur in traditional FFT-based activity recognition. Data training can enable personalised movement analysis that often impact reality alignment. The proposed Digital Twin can enable auto-intervention and interaction for recommendations and can dynamically manage the IoT system.