AIMC Topic: Neurological Rehabilitation

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Rehabilitation, neuroplasticity, and machine learning: Approaching artificial intelligence for equitable health systems.

Neuroscience
Recently, technology has evolved significantly in the rehabilitation process for neurological disorders and neurodegenerative diseases, focusing on neuroplasticity. Neuroplasticity, as a fundamental base of brain rehabilitation, is the change in the ...

A multifaceted hybrid ES-robotic device for gait training in individuals with neurological disorders.

Nature communications
The integration of robotics and Electrical Stimulation (ES) in neurorehabilitation leverages robotics' precise task execution alongside ES-induced motor learning, muscle conditioning, and cardiovascular benefits. We propose a hybrid system for overgr...

Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke.

Bioscience trends
Stroke remains a leading cause of mortality and long-term disability worldwide, frequently resulting in impairments in motor control, cognition, and emotional regulation. Conventional rehabilitation approaches, while partially effective, often lack i...

Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions.

Journal of neuroengineering and rehabilitation
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) ...

Neurorehabilitation in spinal cord injury: Increased cortical activity through tDCS and robotic gait training.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: This study investigates the neurophysiological outcomes of combining robot-assisted gait training (RAGT) with active transcranial direct current stimulation (tDCS) on individuals with spinal cord injury (SCI).

A deep learning model for assistive decision-making during robot-aided rehabilitation therapies based on therapists' demonstrations.

Journal of neuroengineering and rehabilitation
BACKGROUND: A promising approach to improving motor recovery during rehabilitation is the use of robotic rehabilitation devices. These robotic devices provide tools to monitor the patient's recovery progress while providing highly standardized and in...

Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation.

Journal of neuroengineering and rehabilitation
BACKGROUND: Robot-Assisted Gait Rehabilitation (RAGR) is an established clinical practice to encourage neuroplasticity in patients with neuromotor disorders. Nevertheless, tasks repetition imposed by robots may induce boredom, affecting clinical outc...

Effectiveness of robotic rehabilitation for gait and balance in people with multiple sclerosis: a systematic review.

Journal of neurology
This review investigated the effectiveness of robotic-assisted gait training (RAGT) in improving gait and balance performance in adults with multiple sclerosis (MS). Databases and registers were searched from inception to December 2023 to identify ra...

Assessment of wearable robotics performance in patients with neurological conditions.

Current opinion in neurology
PURPOSE OF REVIEW: While wearable robotics is expanding within clinical settings, particularly for neurological rehabilitation, there is still a lack of consensus on how to effectively assess the performance of these devices. This review focuses on t...

A genetic algorithm-based method to modulate the difficulty of serious games along consecutive robot-assisted therapy sessions.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: One of the biggest challenges during neurorehabilitation therapies is finding an appropriate level of therapy intensity for each patient to ensure the recovery of movement of the affected limbs while maintaining motivation. ...