Exploring the Impact of Artificial Intelligence on Advancing Neurorehabilitation Techniques: A Comprehensive Survey in Biomedical Engineering

In recent decades, neurorehabilitation, a key area in biomedical engineering, has increasingly focused on helping people with movement disorders recover lost functions. However, the capabilities of artificial intelligence (AI) have paved the way for new advances in enhancing these treatments. This paper reviews the current status of artificial intelligence-based neurorehabilitation techniques, emphasizing the roles of machine learning (ML), deep learning (DL), Functional Electrical Stimulation (FES) methods, assistive robots, and biofeedback systems. Additionally, artificial intelligence techniques, including electromyography (EMG) and electroencephalography (EEG) data processing, human motion monitoring, and adaptive robotic support, have shown considerable promise in optimizing and personalizing therapeutic interventions. Despite these advances, important challenges remain, such as ensuring data integrity, improving the generalizability of AI models, addressing ethical issues, and effectively integrating AI technologies into clinical practice. Overcoming these barriers is critical to implementing AI innovations in clinical settings. This study also explores potential research avenues aimed at addressing these challenges and further increasing the impact of AI in neurorehabilitation.

کلیدواژه ها:

Artificial Intelligence ، Biomedical Engineering ، Machine Learning ، Neurorehabilitation