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Rehabilitation exoskeletons face acceptability issues due to their inability to produce trajectories representing therapists’ desires. Learning by Demonstration can offer a solution to this issue by using therapists’ demonstrations to teach the robot how to perform movements. While various methods for teaching movements to a robot from human demonstrations and manipulation have been proposed, no discussion about the best one for rehabilitation purposes has been conducted. Our work aims to discuss a Hidden-Markov-Model-based (HMM) approach, integrated with a human-likeness and a joint-synchronization algorithm, and compare it with two other well-known approaches we previously validated for rehabilitation. Applying the three methods to some therapists-generated datasets, produced with the exoskeleton AGREE and representing 5 different exercises, we verified that our HMM approach is better both in terms of human likeness (as measured by the SPARC metric), and compliance with therapy time requirements (needing less than a second of computation).

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Learn from Therapists’ Demonstrations Approaches for Robotic Rehabilitation Exercises

  • 会议论文

  • 发表日期：

    2025年

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  • Beatrice Luciani,
  • Alessandra Pedrocchi,
  • Francesco Braghin,
  • Marta Gandolla

摘要

Rehabilitation exoskeletons face acceptability issues due to their inability to produce trajectories representing therapists’ desires. Learning by Demonstration can offer a solution to this issue by using therapists’ demonstrations to teach the robot how to perform movements. While various methods for teaching movements to a robot from human demonstrations and manipulation have been proposed, no discussion about the best one for rehabilitation purposes has been conducted. Our work aims to discuss a Hidden-Markov-Model-based (HMM) approach, integrated with a human-likeness and a joint-synchronization algorithm, and compare it with two other well-known approaches we previously validated for rehabilitation. Applying the three methods to some therapists-generated datasets, produced with the exoskeleton AGREE and representing 5 different exercises, we verified that our HMM approach is better both in terms of human likeness (as measured by the SPARC metric), and compliance with therapy time requirements (needing less than a second of computation).