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Updated: Apr 28, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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Optimizing human-in-the-loop training: Real-time personalized task scheduling via model predictive control.

Arash Salemi1, Amirhossein Afkhami Ardekani1, Albert H Vette2

  • 1Department of Mechanical Engineering, University of Alberta, Donadeo Innovation Centre for Engineering, Edmonton, Alberta, T6G 1H9, Canada.

Computers in Biology and Medicine
|April 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new real-time task scheduling framework using Moving Horizon Estimation (MHE) and Model Predictive Control (MPC) for motor learning. The new method significantly improves long-term retention and adaptation in training, outperforming traditional schedules.

Keywords:
Adaptive human-in-the-loop trainingDual-rate motor learning modelModel predictive controlMoving horizon estimationPersonalized robotic trainingTask scheduling

Related Experiment Videos

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Area of Science:

  • Motor learning and control
  • Robotics and human-in-the-loop systems
  • Rehabilitation engineering

Background:

  • Effective motor learning is crucial for rehabilitation and professional training, with task scheduling impacting multi-task learning.
  • The dual-rate motor learning model highlights a slow-learning, slow-forgetting state crucial for long-term retention.
  • Existing task scheduling methods lack optimality or real-time applicability due to rule-based or offline optimization approaches.

Purpose of the Study:

  • To develop a novel, control-theory-based framework for real-time optimization of task schedules in human-in-the-loop training.
  • To integrate Moving Horizon Estimation (MHE) and Model Predictive Control (MPC) for optimizing task sequences to maximize the slow-learning state.
  • To enhance long-term retention and adaptation in motor skill acquisition.

Main Methods:

  • Leveraged Moving Horizon Estimation (MHE) for real-time state and parameter estimation of the dual-rate motor learning model.
  • Employed Model Predictive Control (MPC) to utilize MHE estimates for selecting optimal task sequences.
  • Benchmarked MPC against an Alternating schedule and offline optimization in simulations and experiments (n=20).

Main Results:

  • MPC demonstrated superior performance in simulations, achieving up to 27% lower cost function values compared to the Alternating schedule.
  • Experimental results showed the MPC group had up to 46% lower mean motor errors in later testing phases.
  • The MPC group exhibited significantly faster adaptation and stronger long-term retention (p < 0.05).

Conclusions:

  • The developed MHE-MPC framework enables real-time, personalized optimization of training schedules for motor learning.
  • This approach significantly enhances adaptation and long-term retention compared to traditional methods.
  • The findings indicate a transformative potential for next-generation rehabilitation and high-performance skill acquisition.