Learning Varying Physical Therapist-Patient Interactions for Robot-mediated Upper Limb Task-Specific Training

2026-08-16

AI Summary

This study addresses the lack of personalized interaction and training variability in rehabilitation robotics by proposing a Learning from Demonstration framework based on Task-Parameterized Gaussian Mixture Models (TPGMM). By modeling the mapping from joint kinematics to torques, this method enables effective generalization of personalized therapeutic strategies to novel task variants under few-shot conditions. Validation across 14 simulated interactions demonstrates that TPGMM achieves superior reproduction accuracy on unseen variants compared to lookup table methods, with performance improving as task complexity increases. Consequently, this research significantly enhances the adaptability and efficacy of upper-limb task-specific training, establishing a new paradigm for personalized robotic rehabilitation.

Abstract

Upper extremity motor function recovery is positively linked to Task-Specific Training (TST) and sufficient therapy dosage. Rehabilitation robots can increase TST dosage via controlled, repetitive treatment and free therapists to simultaneously manage other patients, but it has yet to demonstrate significant benefits over conventional treatment. This is potentially linked to inaccurate robotic representation of personalised physical therapist-patient interaction and lack of practice variability during TST. Hence, we advocate for robotic interventions that preserve the personalised physical therapist-patient interactions when delivering TST for patients across varying practise conditions. We propose a Learning-from-Demonstration framework using Task-Parameterised Gaussian Mixture Models (TPGMM) to learn personalised physical therapist-patient interaction in Task-Specific exercises, mapping patient joint kinematics to therapist-applied torques using few demonstrations. The model is generalised to reconstruct therapist torques in new task variations. The framework was evaluated on physical interactions from 14 mock "therapist-patient" pairs over three tasks of increasing complexity, each with six variations. A benchmark comparison against a Look-Up Table was conducted. The results show both methods reproducing interactions in unseen task variations that deviate slightly from the actual interaction, with TPGMM slightly outperforming LUT. Both methods reproduced interactions that gets increasingly closer to the actual interaction as task complexity increases.

Problem

• Robot-mediated rehabilitation
• Task-Specific Training
• Therapist-patient interaction
• Practice variability
• Upper limb motor recovery

Innovation

• Task-Parameterised Gaussian Mixture Models
• Learning-from-Demonstration
• Personalized Therapist-Patient Interaction
• Robot-mediated Rehabilitation
• Task-Specific Training