Background: Recent advancement in the technology of virtual reality (VR) has allowed improved applications for cognitive rehabilitation.
Objectives: The aim of this review is to facilitate comparisons of therapeutic efficacy of different VR interventions.
Methods: A systematic approach for the review of VR cognitive rehabilitation outcome research addressed the nature of each sample, treatment apparatus, experimental treatment protocol, control treatment protocol, statistical analysis and results. Using this approach, studies that provide valid evidence of efficacy of VR applications are summarized. Applications that have not yet undergone controlled outcome study but which have promise are introduced.
Results: Seventeen studies conducted over the past eight years are reviewed. The few randomized controlled trials that have been completed show that some applications are effective in treating cognitive deficits in people with neurological diagnoses although further study is needed.
Conclusion: Innovations requiring further study include the use of enriched virtual environments that provide haptic sensory input in addition to visual and auditory inputs and the use of commercially available gaming systems to provide tele-rehabilitation services. Recommendations are offered to improve efficacy of rehabilitation, to improve scientific rigor of rehabilitation research and to broaden access to the evidence-based treatments that this research has identified.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.3233/NRE-141078 | DOI Listing |
Nanophotonics
January 2025
Key Laboratory for Information Science of Electromagnetic Waves, School of Information Science and Technology, Fudan University, Shanghai 200433, China.
Gesture recognition plays a significant role in human-machine interaction (HMI) system. This paper proposes a gesture-controlled reconfigurable metasurface system based on surface electromyography (sEMG) for real-time beam deflection and polarization conversion. By recognizing the sEMG signals of user gestures through a pre-trained convolutional neural network (CNN) model, the system dynamically modulates the metasurface, enabling precise control of the deflection direction and polarization state of electromagnetic waves.
View Article and Find Full Text PDFCureus
December 2024
Department of Ophthalmology, Broward Health, Fort Lauderdale, USA.
This literature review explores the emerging role of digital twin (DT) technology in ophthalmology, emphasizing its potential to revolutionize personalized medicine. DTs integrate diverse data sources, including genetic, environmental, and real-time patient data, to create dynamic, predictive models that enhance risk assessment, surgical planning, and postoperative care. The review highlights vital case studies demonstrating the application of DTs in improving the early detection and management of diseases such as glaucoma and age-related macular degeneration.
View Article and Find Full Text PDFFront Psychol
January 2025
Neurointerfaces and Neurotechnologies Laboratory, Neurosciences Research Institute, Samara State Medical University, Samara, Russia.
Metaverse integrates people into the virtual world, and challenges depend on advances in human, technological, and procedural dimensions. Until now, solutions to these challenges have not involved extensive neurosociological research. The study explores the pioneering neurosociological paradigm in metaverse, emphasizing its potential to revolutionize our understanding of social interactions through advanced methodologies such as hyperscanning and interbrain synchrony.
View Article and Find Full Text PDFFront Psychol
January 2025
Department of Psychology, Università degli Studi di Torino, Turin, Italy.
Front Child Adolesc Psychiatry
September 2023
Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada.
Introduction: Attention-deficit/hyperactivity disorder (ADHD) and autism are multi-faceted neurodevelopmental conditions with limited biological markers. The clinical diagnoses of autism and ADHD are based on behavioural assessments and may not predict long-term outcomes or response to interventions and supports. To address this gap, data-driven methods can be used to discover groups of individuals with shared biological patterns.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!