Year: 2025
Type: article
Abstract: Neurorehabilitation conventionally relies on the interaction between a patient and a physical therapist. Robotic systems can improve and enrich the physical feedback provided to patients after neurological injury, but they under-utilize the adaptability and clinical expertise of trained therapists. In this position paper, we advocate for a novel approach that integrates the therapist's clinical expertise and nuanced decision-making with the strength, accuracy, and repeatability of robotics: Robot-mediated physical Human-Human Interaction. This framework, which enables two individuals to physically interact through robotic devices, has been studied across diverse research groups and has recently emerged as a promising link between conventional manual therapy and rehabilitation robotics, harmonizing the strengths of both approaches. Although current findings are largely based on pilot studies and conceptual frameworks, integrating therapists' expertise with the functionalities offered by robotic systems represents a promising direction for improving rehabilitation outcomes. This paper presents the rationale of a multidisciplinary team-including engineers, doctors, and physical therapists-for conducting research that utilizes: a unified taxonomy to describe robot-mediated rehabilitation, a framework of interaction based on social psychology, and a technological approach that makes robotic systems seamless facilitators of natural human-human interaction.
Authors: Lorenzo Vianello, Matthew Short, Julia Manczurowsky, Emek Barş Küçüktabak, Francesco Di Tommaso, Alessia Noccaro, Laura Bandini, Shoshana Clark, Alaina Fiorenza, Francesca Lunardini, Alberto Canton, Marta Gandolla, Alessandra L. G. Pedrocchi, Emilia Ambrosini, Manuel Murie-Fernández, Carmen B. Román, Jesus Tornero, Natacha Leon, Andrew Sawers, Jim Patton, Domenico Formica, Nevio Luigi Tagliamonte, Georg Rauter, Kilian Baur, Fabian Just, Christopher J. Hasson, Vesna D. Novak, Jose L. Pons
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4416738878