Employing Socially Interactive Agents for Robotic Neurorehabilitation Training

Rhythm Arora; Matteo Lavit Nicora; Pooja Prajod; Daniele Panzeri; Elisabeth André; Patrick Gebhard; Matteo Malosio

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arXiv · 2206.01587

Employing Socially Interactive Agents for Robotic Neurorehabilitation Training

Rhythm Arora·Matteo Lavit Nicora·Pooja Prajod·Daniele Panzeri·Elisabeth André·Patrick Gebhard·Matteo Malosio

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Abstract

In today's world, many patients with cognitive impairments and motor dysfunction seek the attention of experts to perform specific conventional therapies to improve their situation. However, due to a lack of neurorehabilitation professionals, patients suffer from severe effects that worsen their condition. In this paper, we present a technological approach for a novel robotic neurorehabilitation training system. It relies on a combination of a rehabilitation device, signal classification methods, supervised machine learning models for training adaptation, training exercises, and socially interactive agents as a user interface. Together with a professional, the system can be trained towards the patient's specific needs. Furthermore, after a training phase, patients are enabled to train independently at home without the assistance of a physical therapist with a socially interactive agent in the role of a coaching assistant.

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Rhythm Arora, Matteo Lavit Nicora, Pooja Prajod, Daniele Panzeri, Elisabeth André, Patrick Gebhard, Matteo Malosio. 2022-06-03. Employing Socially Interactive Agents for Robotic Neurorehabilitation Training. https://arxiv.org/abs/2206.01587

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