Unidirectional Human-Robot-Human Physical Interaction for Gait Training
Lorenzo Amato, Lorenzo Vianello, Emek Baris Kucuktabak, Clement, Lhoste, Matthew Short, Daniel Ludvig, Kevin Lynch, Levi Hargrove, and Jose L., Pons

TL;DR
This paper introduces a novel unidirectional human-robot-human interaction framework for gait training, enabling therapists to guide patients using real-time impedance control via IMU suits and exoskeletons, reducing physical effort and eliminating predefined trajectories.
Contribution
It presents a new virtual interaction framework with tailored impedance profiles for effective gait training, demonstrated through experiments with unimpaired individuals.
Findings
Effective transmission of haptic cues in gait training
Reduced physical effort for therapists during rehabilitation
Flexible impedance profiles tailored to patient needs
Abstract
This work presents a novel rehabilitation framework designed for a therapist, wearing an inertial measurement unit (IMU) suit, to virtually interact with a lower-limb exoskeleton worn by a patient with motor impairments. This framework aims to harmonize the skills and knowledge of the therapist with the capabilities of the exoskeleton. The therapist can guide the patient's movements by moving their own joints and making real-time adjustments to meet the patient's needs, while reducing the physical effort of the therapist. This eliminates the need for a predefined trajectory for the patient to follow, as in conventional robotic gait training. For the virtual interaction medium between the therapist and patient, we propose an impedance profile that is stiff at low frequencies and less stiff at high frequencies, that can be tailored to individual patient needs and different stages of…
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Taxonomy
TopicsHand Gesture Recognition Systems · Robot Manipulation and Learning · Gait Recognition and Analysis
