A Systematic Review of Machine Learning in Robotics-Assisted Rehabilitation DOI Creative Commons
Giovanna Nicora,

Samuele Pe,

Gabriele Santangelo

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 3, 2024

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

Language: Английский

A Systematic Review of Machine Learning in Robotics-Assisted Rehabilitation DOI Creative Commons
Giovanna Nicora,

Samuele Pe,

Gabriele Santangelo

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 3, 2024

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

Language: Английский

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