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Summary

This study introduces a new framework using Internet of Things (IoT), Digital Twins (DT), and machine learning/Artificial Intelligence (ML/AI) to improve prehabilitation for surgery patients. It enables personalized, adaptive remote monitoring and interventions for better recovery.

Keywords:
IoTartificial intelligencedigital twinhuman movement monitoringprehabilitationwearable sensors

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Area of Science:

  • Digital Health
  • Biomedical Engineering
  • Rehabilitation Medicine

Background:

  • Prehabilitation programs enhance surgical outcomes, but current IoT monitoring lacks personalization and real-time integration.
  • Mixed-mode prehabilitation combines clinical supervision with home-based care, improving accessibility but facing challenges in remote monitoring.
  • Existing systems often require manual oversight, limiting the effectiveness of interventions and patient outcomes.

Purpose of the Study:

  • To propose a conceptual framework integrating Digital Twin (DT) technology and Machine Learning/Artificial Intelligence (ML/AI) to enhance IoT-based mixed-mode prehabilitation programs.
  • To address limitations in current IoT systems, including lack of personalized analysis, adaptive interventions, and real-time clinical integration.
  • To improve functional outcomes and enable dynamic, remote supervision for pre-operative patients undergoing prehabilitation.

Main Methods:

  • Utilizing inertial sensors in wearable devices and smartphones for continuous movement data collection during prehabilitation exercises.
  • Employing advanced ML/AI algorithms for precise classification of activity types and intensities, surpassing traditional methods like Fast Fourier Transform (FFT).
  • Integrating a Digital Twin (DT) to monitor IoT behavior, simulate patient-specific movement profiles, and enable automated, real-time adjustments and interventions.

Main Results:

  • The proposed framework leverages ML/AI for accurate movement analysis, overcoming limitations of FFT-based methods.
  • The Digital Twin facilitates continuous monitoring, personalized interventions, and adaptive adjustments based on real-time patient data.
  • The integration enables bidirectional communication, supporting dynamic and remote supervision between patients and clinicians.

Conclusions:

  • The combined IoT, DT, and ML/AI framework offers a scalable and novel approach to personalized pre-operative care.
  • This integrated system addresses current limitations in remote prehabilitation monitoring and intervention.
  • The proposed solution enhances patient outcomes and recovery by providing adaptive, data-driven prehabilitation support.