A Systematic Review of Machine Learning in Robotics-Assisted Rehabilitation

Author:

Nicora Giovanna1,Pe Samuele1,Santangelo Gabriele1,Billeci Lucia2,Aprile Irene Giovanna3,Germanotta Marco3,Bellazzi Riccardo1,Parimbelli Enea1,Quaglini Silvana1

Affiliation:

1. University of Pavia

2. Istituto di Fisiologia Clinica, Consiglio Nazionale delle Ricerche (IFC-CNR)

3. Fondazione Don Gnocchi

Abstract

Abstract

Robotic technology is expected to transform rehabilitation settings, by providing precise, repetitive, and task-specific interventions, thereby potentially improving patients’ clinical outcomes. Artificial intelligence (AI) and machine learning (ML) have been widely applied in different areas to support robotic rehabilitation, from controlling robot movements to real-time patient assessment. To provide and overview the current landscape and the impact of AI/ML use in robotics rehabilitation, we performed a systematic review focusing on the use of AI and robotics in rehabilitation from a broad perspective, encompassing different pathologies and body districts, and considering both motor and neurocognitive rehabilitation. We searched the Scopus and IEEE Xplore databases, focusing on the studies involving human participants. After article retrieval, a tagging phase was carried out to devise a comprehensive and easily-interpretable taxonomy: its categories include the aim of the AI/ML within the rehabilitation system, the type of algorithms used, and the location of robots and sensors. The selected articles span multiple domains and diverse aims, such as movement classification, trajectory prediction, and patient evaluation, demonstrating the potential of ML to revolutionize personalized therapy and improve patient engagement. ML is reported as highly effective in predicting movement intentions, assessing clinical outcomes, and detecting compensatory movements, providing insights into the future of personalized rehabilitation interventions. Our analysis also reveals pitfalls in the current use of AI/ML in this area, such as potential explainability issues and poor generalization ability when these systems are applied in real-world settings.

Publisher

Springer Science and Business Media LLC

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