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

• URL: http://arxiv.org/abs/2608.15995v1
• Date: Mon, 17 Aug 2026 01:16:35 GMT
• Title: Learning Varying Physical Therapist-Patient Interactions for Robot-mediated Upper Limb Task-Specific Training
• Abstract summary: Upper extremity motor function recovery is positively linked to Task-Specific Training (TST) and sufficient therapy dosage.<n> Rehabilitation robots can increase TST dosage via controlled, repetitive treatment and free therapists to simultaneously manage other patients.<n>This is potentially linked to inaccurate robotic representation of personalised physical therapist-patient interaction and lack of practice variability.
• Score: 3.433344218375448
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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