Machine learning for Developing neurorehabilitation-aided assistive devices

Goyal, Shivalika; Laddi, Amit

doi:10.1016/b978-0-443-13772-3.00007-8

Computational Intelligence and Deep Learning Methods for Neuro-Rehabilitation Applications 2024

DOI: 10.1016/b978-0-443-13772-3.00007-8

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Machine learning for Developing neurorehabilitation-aided assistive devices

Shivalika Goyal,

Amit Laddi

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2024

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Cited by 2 publications

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Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders

Calderone,

Latella,

Bonanno

et al. 2024

Biomedicines

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Background and Objectives: Neurological disorders like stroke, spinal cord injury (SCI), and Parkinson’s disease (PD) significantly affect global health, requiring accurate diagnosis and long-term neurorehabilitation. Artificial intelligence (AI), such as machine learning (ML), may enhance early diagnosis, personalize treatment, and optimize rehabilitation through predictive analytics, robotic systems, and brain-computer interfaces, improving outcomes for patients. This systematic review examines how AI and ML systems influence diagnosis and treatment in neurorehabilitation among neurological disorders. Materials and Methods: Studies were identified from an online search of PubMed, Web of Science, and Scopus databases with a search time range from 2014 to 2024. This review has been registered on Open OSF (n) EH9PT. Results: Recent advancements in AI and ML are revolutionizing motor rehabilitation and diagnosis for conditions like stroke, SCI, and PD, offering new opportunities for personalized care and improved outcomes. These technologies enhance clinical assessments, therapy personalization, and remote monitoring, providing more precise interventions and better long-term management. Conclusions: AI is revolutionizing neurorehabilitation, offering personalized, data-driven treatments that enhance recovery in neurological disorders. Future efforts should focus on large-scale validation, ethical considerations, and expanding access to advanced, home-based care.

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Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders

Calderone,

Latella,

Bonanno

et al. 2024

Biomedicines

View full text Add to dashboard Cite

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Brain-Computer Interfaces Enhanced by AI: Applications in Rehabilitation and Assistive Technology

Kılıç

2024

NFLSAI

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Brain-Computer Interfaces (BCIs) enhanced by Artificial Intelligence (AI) represent a transformative frontier in rehabilitation and assistive technology. These systems enable direct communication between the brain and external devices, empowering individuals with neurological impairments to regain lost functions and enhance their quality of life. By integrating AI, BCIs can decode complex neural signals with unprecedented accuracy, enabling applications such as motor function restoration, cognitive enhancement, and assistive communication. This research explores the current state of AI-driven BCIs, focusing on their impact on rehabilitation for stroke survivors, individuals with spinal cord injuries, and those with neurodegenerative disorders. Ethical challenges, such as data privacy, consent, and accessibility, are also examined. Through a review of case studies and emerging trends, this study highlights the potential of AI-enhanced BCIs to revolutionize neurorehabilitation and foster greater independence for individuals with disabilities.

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Machine learning for Developing neurorehabilitation-aided assistive devices

Cited by 2 publications

References 90 publications

Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders

Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders

Brain-Computer Interfaces Enhanced by AI: Applications in Rehabilitation and Assistive Technology

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