Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review

Actuators, Journal Year: 2025, Volume and Issue: 14(1), P. 16 - 16

Published: Jan. 6, 2025

The integration of neural networks into robotic exoskeletons for physical rehabilitation has become popular due to their ability interpret complex physiological signals. Surface electromyography (sEMG), (EMG), electroencephalography (EEG), and other signals enable communication between the human body systems. Utilizing communicating with robots plays a crucial role in robot-assisted neurorehabilitation. This systematic review synthesizes 44 peer-reviewed studies, exploring how can improve exoskeleton individuals impaired upper limbs. By categorizing studies based on joints, sensor systems, control methodologies, we offer comprehensive overview network applications this field. Our findings demonstrate that networks, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Radial Basis Function (RBFNNs), forms significantly contribute patient-specific by enabling adaptive learning personalized therapy. CNNs motion intention estimation accuracy, while LSTM capture temporal muscle activity patterns real-time rehabilitation. RBFNNs human–robot interaction adapting individual movement patterns, leading more efficient highlights potential revolutionize limb rehabilitation, improving motor recovery patient outcomes both clinical home-based settings. It also recommends future direction customizing existing applications.

Language: Английский

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Digital Twins Generated by Artificial Intelligence in Personalized Healthcare

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(20), P. 9404 - 9404

Published: Oct. 15, 2024

Digital society strategies in healthcare include the rapid development of digital twins (DTs) for patients and human organs medical research use artificial intelligence (AI) clinical practice to develop effective treatments a cheaper, quicker, more manner. This is facilitated by availability large historical datasets from previous trials other real-world data sources (e.g., patient biometrics collected wearable devices). DTs can AI models create predictions future health outcomes an individual form AI-generated twin support assessment silico intervention strategies. are gaining ability update real time relation their corresponding physical connect multiple diagnostic therapeutic devices. Support this personalized medicine necessary due complex technological challenges, regulatory perspectives, issues security trust approach. The challenge also combine different omics quickly interpret order generate disease indicators improve sampling longitudinal analysis. It possible care through various means (simulated trials, prediction, remote monitoring apatient’s condition, treatment progress, adjustments plan), especially environments smart cities territories wider 6G, blockchain (and soon maybe quantum cryptography), Internet Things (IoT), as well technologies, such multiomics. From practical point view, requires not only efficient validation but seamless integration with existing infrastructure.

Language: Английский

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Overview of 3D Printed Exoskeleton Materials and Opportunities for Their AI-Based Optimization

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(14), P. 8384 - 8384

Published: July 20, 2023

An aging population, the effects of pandemics and civilization-related conditions, limited leapfrogging in number rehabilitation physiotherapy specialists are driving demand for modern assistive technologies, especially upper lower limb exoskeletons. Patient-tailored devices a rapidly developing group both from biomechanics, informatics, materials engineering perspective. In particular, technological development 3D printing, expanding range available their properties (including contact with living tissue bodily fluids), possibility selecting optimizing them using artificial intelligence machine learning) encouraging emergence new concepts, particularly within Industry 4.0 paradigm. The article provides an overview what is this area, including assessment as yet untapped research industrial and, part, clinical potential.

Language: Английский

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Applications of Artificial Intelligence-Based Patient Digital Twins in Decision Support in Rehabilitation and Physical Therapy

Electronics, Journal Year: 2024, Volume and Issue: 13(24), P. 4994 - 4994

Published: Dec. 19, 2024

Artificial intelligence (AI)-based digital patient twins have the potential to make breakthroughs in research and clinical practices rehabilitation. They it possible personalise treatment plans by simulating different rehabilitation scenarios predicting patient-specific outcomes. DTs can continuously monitor a patient’s progress, adjusting therapy real time optimise recovery. also facilitate remote providing virtual models that therapists use guide patients without having be physically present. Digital (DTs) help identify complications or failures at an early stage, enabling proactive interventions. support training of professionals offering realistic simulations conditions. increase engagement visualising progress future outcomes, motivating adherence therapy. enable integration multidisciplinary care, common platform for collaborate improve strategies. The article aims trace current state knowledge, priorities, gaps order properly further shape decision

Language: Английский

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