A narrative review of AI-driven stroke rehabilitation systems through the lens of human motor learning

 I can't tell if this got anywhere closer to getting survivors recovered!

A narrative review of AI-driven stroke rehabilitation systems through the lens of human motor learning

• 

  Zakaria Taghi 1*

• 

  Deborah J. Serrien 2

• 

  Lucas Fonseca 1

• 

  Nikhil Deshpande 1

• 1. School of Computer Science, University of Nottingham, Nottingham, United Kingdom

• 2. School of Psychology, University of Nottingham, Nottingham, United Kingdom

Abstract

Introduction: 

Stroke represents a leading global cause of disability, often causing motor impairments that diminish quality of life. Neurorehabilitation that leverages human motor learning (HML) theories is crucial for post-stroke recovery. Therapists guide repetitive practice that supports re-learning, and they adjust assistance to individual needs and progress. Robot-assisted rehabilitation has advanced this approach, and recent work shows AI-driven systems can improve adaptability to patient behavior beyond earlier technologies. However, notably few systems aim to explicitly replicate therapist assistance from the perspective that physical assistance is a motor skill in itself.

Methodology: 

This narrative review examines advances in AI-driven stroke rehabilitation, analyzing how systems facilitate HML within patients and how their models approximate HML mechanisms. By breaking down the four core HML processes to their essentials, using Marr's tri-level hypothesis, we compare machine learning models used within rehabilitation systems to the HML processes.

Results: 

Many of the reviewed systems appear to primarily facilitate use-dependent and sensory-prediction error-based learning, with limited facilitation of reinforcement learning or strategy-based learning. Explicit modeling of therapist HML within control frameworks appears relatively rare. Implicitly, many of the reviewed AI systems functionally represent one or two HML processes.

Conclusion: 

Current research often considers HML primarily in patients, whereas therapists' own HML likely underpins the robustness and adaptability of clinical assistance. Interpreting the reviewed rehabilitation systems through this lens highlights opportunities for therapist-inspired multi-process controllers, improved benchmarking with clinical scales, longitudinal retention studies, and AI-driven closed-loop neuromodulation to enhance personalization, adaptability, and outcomes, and to support clinical translation into routine practice.