AIMC Topic: Stroke Rehabilitation

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Towards AI-based precision rehabilitation via contextual model-based reinforcement learning.

Journal of neuroengineering and rehabilitation
BACKGROUND: Stroke is a condition marked by considerable variability in lesions, recovery trajectories, and responses to therapy. Consequently, precision medicine in rehabilitation post-stroke, which aims to deliver the "right intervention, at the ri...

Muscle synergy-driven ensemble learning framework for individualized stroke gait rehabilitation.

Scientific reports
This study proposes a novel ensemble machine learning (ML) framework integrating neurophysiological principles from muscle synergy analysis to support clinical decisions in stroke gait rehabilitation. The framework leverages spatial and temporal feat...

Towards an RGB camera-based live repetition counter using auto correlation with action recognition for home rehabilitation.

Scientific reports
Patients recovering from stroke often struggle with their rehabilitation exercises at home, with personal therapists being both costly and potentially unavailable. Virtual rehabilitation programs may assist the patients by providing quantitative and ...

Functional connectivity between non-motor and motor networks predicts motor recovery changes after stroke.

Scientific reports
Stroke impairs limb motor function, which affects patients' quality of life and imposes economic burdens. Early prediction of motor recovery is essential for guiding treatment and rehabilitation. While the corticospinal tract is a known biomarker, th...

Configuration design of ankle rehabilitation robots: a systematic review and future prospects.

Journal of neuroengineering and rehabilitation
Compared to traditional therapist-assisted rehabilitation training, ankle rehabilitation robots (ARRs) can offer a wider variety of active and passive training strategies for stroke and fracture patients. This enhances patient recovery outcomes and e...

Effect of upper-limb robot-assisted therapy combined with pneumatic gloves on upper limb function in young and middle-aged stroke patients: a pilot randomized controlled trial.

Journal of neuroengineering and rehabilitation
BACKGROUND: This study aimed to evaluate the effects of an end-effector-type upper-limb robot-assisted therapy (UL-RAT) combined with pneumatic gloves (PGs) on improving upper limb function in young and middle-aged stroke patients.

Early adherence to biofeedback training predicts long-term improvement in stroke patients: A machine learning approach.

PloS one
Biofeedback-based treadmill training generally involves 10 or more sessions to assess its effectiveness during stroke rehabilitation. Improvements are seen in some patients during the assessment, while others do not progress. Our aim in this study is...

Advancing post-stroke blood pressure management: an individualized BP strategy for function optimization.

Annals of medicine
BACKGROUND: Stroke remains a major global public health concern and a leading cause of death, disability, and dementia. Despite being the most important and modifiable risk factor for stroke, Blood pressure (BP) management remain controversial and ch...

The effects of combining anodal transcranial direct current stimulation with robot-assisted gait training on lower limb motor function and the motor cortex regulation of stroke patients.

Journal of neuroengineering and rehabilitation
BACKGROUND: The therapeutic effect and underlying mechanism of combining transcranial direct current stimulation (tDCS) with robot-assisted gait training (RAGT) for stroke patients remain unclear.

Can machine learning improve on the early prediction of upper limb recovery after stroke?

Journal of neuroengineering and rehabilitation
BACKGROUND: Early prediction of upper limb recovery is important to optimise rehabilitation and inform patients but remains challenging due to inter-individual variability. This study aims to (1) develop and validate a machine learning model to predi...