Employing Socially Interactive Agents for Robotic Neurorehabilitation Training

TL;DR

This paper introduces a robotic neurorehabilitation system that combines machine learning, interactive agents, and personalized training to improve therapy accessibility and effectiveness for patients with cognitive and motor impairments.

Contribution

It presents a novel integrated system utilizing socially interactive agents and adaptive machine learning for personalized neurorehabilitation training.

Findings

System enables independent home training for patients.

Combines signal classification with social agents for improved engagement.

Supports personalized therapy adaptation.

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.