Healthcare (Basel, Switzerland) · 2025
Assistive Technologies for Individuals with a Disability from a Neurological Condition: A Narrative Review on the Multimodal Integration.
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Bonanno Mirjam, Saracino Beatrice, Ciancarelli Irene, Panza Giuseppe, Manuli Alfredo, Morone Giovanni, Calabrò Rocco Salvatore
Аннотация
BACKGROUND/OBJECTIVES: Neurological disorders often result in a broad spectrum of disabilities that impact mobility, communication, cognition, and sensory processing, leading to significant limitations in independence and quality of life. Assistive technologies (ATs) offer tools to compensate for these impairments, support daily living, and improve quality of life. The World Health Organization encourages the adoption and diffusion of effective assistive technology (AT). This narrative review aims to explore the integration, benefits, and challenges of assistive technologies in individuals with neurological disabilities, focusing on their role across mobility, communication, cognitive, and sensory domains.
METHODS: A narrative approach was adopted by reviewing relevant studies published between 2014 and 2024. Literature was sourced from PubMed and Scopus using specific keyword combinations related to assistive technology and neurological disorders.
RESULTS: Findings highlight the potential of ATs, ranging from traditional aids to intelligent systems like brain-computer interfaces and AI-driven devices, to enhance autonomy, communication, and quality of life. However, significant barriers remain, including usability issues, training requirements, accessibility disparities, limited user involvement in design, and a low diffusion of a health technology assessment approach.
CONCLUSIONS: Future directions emphasize the need for multidimensional, user-centered solutions that integrate personalization through machine learning and artificial intelligence to ensure long-term adoption and efficacy. For instance, combining brain-computer interfaces (BCIs) with virtual reality (VR) using machine learning algorithms could help monitor cognitive load in real time. Similarly, ATs driven by artificial intelligence technology could be useful to dynamically respond to users' physiological and behavioral data to optimize support in daily tasks.
METHODS: A narrative approach was adopted by reviewing relevant studies published between 2014 and 2024. Literature was sourced from PubMed and Scopus using specific keyword combinations related to assistive technology and neurological disorders.
RESULTS: Findings highlight the potential of ATs, ranging from traditional aids to intelligent systems like brain-computer interfaces and AI-driven devices, to enhance autonomy, communication, and quality of life. However, significant barriers remain, including usability issues, training requirements, accessibility disparities, limited user involvement in design, and a low diffusion of a health technology assessment approach.
CONCLUSIONS: Future directions emphasize the need for multidimensional, user-centered solutions that integrate personalization through machine learning and artificial intelligence to ensure long-term adoption and efficacy. For instance, combining brain-computer interfaces (BCIs) with virtual reality (VR) using machine learning algorithms could help monitor cognitive load in real time. Similarly, ATs driven by artificial intelligence technology could be useful to dynamically respond to users' physiological and behavioral data to optimize support in daily tasks.