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Therapist-Robot-Patient Physical Interaction is Worth a Thousand Words: Enabling Intuitive Therapist Guidance via Remote Haptic Control

Beatrice Luciani, Alex van den Berg, Matti Lang, Alexandre L. Ratschat, Laura Marchal-Crespo

TL;DR

The feasibility of the proposed interface for effective remote human-robot physical interaction is supported and its usability and efficacy for clinical populations in restoring clinicians'sense of agency during robot-assisted therapy is assessed.

Abstract

Robotic systems can enhance the amount and repeatability of physically guided motor training. Yet their real-world adoption is limited, partly due to non-intuitive trainer/therapist-trainee/patient interactions. To address this gap, we present a haptic teleoperation system for trainers to remotely guide and monitor the movements of a trainee wearing an arm exoskeleton. The trainer can physically interact with the exoskeleton through a commercial handheld haptic device via virtual contact points at the exoskeleton's elbow and wrist, allowing intuitive guidance. Thirty-two participants tested the system in a trainer-trainee paradigm, comparing our haptic demonstration system with conventional visual demonstration in guiding trainees in executing arm poses. Quantitative analyses showed that haptic demonstration significantly reduced movement completion time and improved smoothness, while speech analysis using large language models for automated transcription and categorization of verbal commands revealed fewer verbal instructions. The haptic demonstration did not result in higher reported mental and physical effort by trainers compared to the visual demonstration, while trainers reported greater competence and trainees lower physical demand. These findings support the feasibility of our proposed interface for effective remote human-robot physical interaction. Future work should assess its usability and efficacy for clinical populations in restoring clinicians' sense of agency during robot-assisted therapy.

Therapist-Robot-Patient Physical Interaction is Worth a Thousand Words: Enabling Intuitive Therapist Guidance via Remote Haptic Control

TL;DR

The feasibility of the proposed interface for effective remote human-robot physical interaction is supported and its usability and efficacy for clinical populations in restoring clinicians'sense of agency during robot-assisted therapy is assessed.

Abstract

Robotic systems can enhance the amount and repeatability of physically guided motor training. Yet their real-world adoption is limited, partly due to non-intuitive trainer/therapist-trainee/patient interactions. To address this gap, we present a haptic teleoperation system for trainers to remotely guide and monitor the movements of a trainee wearing an arm exoskeleton. The trainer can physically interact with the exoskeleton through a commercial handheld haptic device via virtual contact points at the exoskeleton's elbow and wrist, allowing intuitive guidance. Thirty-two participants tested the system in a trainer-trainee paradigm, comparing our haptic demonstration system with conventional visual demonstration in guiding trainees in executing arm poses. Quantitative analyses showed that haptic demonstration significantly reduced movement completion time and improved smoothness, while speech analysis using large language models for automated transcription and categorization of verbal commands revealed fewer verbal instructions. The haptic demonstration did not result in higher reported mental and physical effort by trainers compared to the visual demonstration, while trainers reported greater competence and trainees lower physical demand. These findings support the feasibility of our proposed interface for effective remote human-robot physical interaction. Future work should assess its usability and efficacy for clinical populations in restoring clinicians' sense of agency during robot-assisted therapy.
Paper Structure (27 sections, 6 equations, 5 figures, 3 tables)

This paper contains 27 sections, 6 equations, 5 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. METHODS
  3. Experimental System: Teleoperation Therapist-in-the-loop Framework
  4. System Architecture
  5. Immersive Virtual Environment
  6. Teleoperation Controller
  7. Feasibility Test: Comparing Haptic and Visual Movement Demonstration
  8. Participants
  9. Experimental Task and Protocol
  10. Outcome Metrics
  11. Motion Outcomes --- Movement Quality and Completion Time
  12. Verbal Interaction Analysis --- Total and Percentual Instructions Provided to Trainees
  13. Subjective Outcomes --- Questionnaire
  14. Statistical Analysis
  15. Results
  16. ...and 12 more sections

Figures (5)

  • Figure 1: Overview of our teleoperation therapist-in-the-loop haptic system. The Trainee (right) wears the ARMin exoskeleton, which is remotely controlled by the Trainer (left) using a Sigma.7 haptic device. The Trainer wears a headset and is immersed in VR, where the digital twin of the exoskeleton and the Trainee’s avatar are visualized. The proposed haptic framework is evaluated in the Haptic Demonstration (HD) condition and compared with a conventional Visual Demonstration (VD) approach. In the VD condition, the Trainer is provided with a webcam, and the Trainee visualizes the Trainer’s movements on a screen in real time. The system includes three interconnected computers, represented in the figure through three rounded rectangular blocks labeled “Host computer”, “Linux computer”, and “Target (ARMin) computer”. In the image, $\vec{X_s}$ denotes the Cartesian position of the Sigma.7 end-effector, $\vec{F_{s,a}}$ is the interaction force computed on the Sigma.7 side by the Teleoperation Controller, $\vec{F_{a}}$ the force computed on the ARMin side, and $J_1,\dots, J_N$ are the ARMin joint positions recorded in real-time.
  • Figure 2: (a) Remote visualization of the virtual environment displayed to the Trainer. The visualization includes the ARMin digital twin ratschat2023 and a representation of the Trainee's arm and torso (male or female). The red spheres on the avatar's wrist and elbow joints represent the graspable points, and the yellow cube is the representation of the end-effector of the Sigma.7 manipulated by the Trainer. (b) Snapshot of the five target arm poses that the Trainee was guided to execute during the experiment.
  • Figure 3: Summary of the results of the speech analysis, with numbers of occurrences for Instruction, Feedback, and Null under Haptic (blue) and Visual (red) conditions. The boxes show the medians and interquartile ranges.
  • Figure 4: Summary of the RTLX scores, divided per dimension and role within the dyad. The boxes show the medians and interquartile ranges. Significant differences, based on the results of the LLMs, are reported: *(p < 0.05), **(p < 0.01), ***(p < 0.001).
  • Figure 5: Average Effort and Perceived Competence subscales of the IMI. Significant differences, based on the results of the LLMs, are reported: *(p < 0.05), **(p < 0.01), ***(p < 0.001).