Rehabilitation robots have the potential to alleviate the global burden of neurorehabilitation. Robot-based multiplayer gaming with virtual and haptic interaction may improve motivation, engagement, and implicit learning in robotic therapy. Over the past few years, there has been growing interest in robot mediated haptic dyads, or human-robot-robot-human interaction. The effect of such a paradigm on motor learning in general and specifically for individuals with motor and/or cognitive impairments is an open area of research. We reviewed the literature to investigate the effect of a robot-based haptic dyad on motor learning. Thirty-eight articles met the inclusion criteria for this review. We summarize study characteristics including device, haptic rendering, and experimental task. Our main findings indicate that dyadic training's impact on motor learning is inconsistent in that some studies show significant improvement of motor training while others show no influence. We also find that the relative skill level of the partner and interaction characteristics such as stiffness of connection and availability of visual information influence motor learning outcomes. We discuss implications for neurorehabilitation and conclude that additional research is needed to determine optimal interaction characteristics for motor learning and to extend this research to individuals with cognitive and motor impairments.
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http://dx.doi.org/10.1109/TOH.2024.3379035 | DOI Listing |
Sci Rep
January 2025
Department of Psychology, Faculty of Psychology and Sport Science, Justus Liebig University, Otto-Behaghel-Str. 10F, 35394, Gießen, Germany.
Adapting movements to rapidly changing conditions is fundamental for interacting with our dynamic environment. This adaptability relies on internal models that predict and evaluate sensory outcomes to adjust motor commands. Even infants anticipate object properties for efficient grasping, suggesting the use of internal models.
View Article and Find Full Text PDFAm J Speech Lang Pathol
January 2025
School of Communication Sciences & Disorders, Elborn College, Western University, London, Ontario Canada.
Purpose: Cerebral palsy (CP) is the most prevalent motor disability affecting children. Many children with CP have significant speech difficulties and require augmentative and alternative communication (AAC) to participate in communication. Despite demonstrable benefits, the use of AAC systems among children with CP remains constrained, although research in Canada is lacking.
View Article and Find Full Text PDFChild Neuropsychol
January 2025
Child Development Center, University Children's Hospital Zurich, Zurich, Switzerland.
Executive function (EF) impairments are prevalent in survivors of neonatal critical illness such as children born very preterm (VPT) or with complex congenital heart disease (cCHD). This paper aimed to describe EF profiles in school-aged children born VPT or with cCHD and in typically developing peers, to identify child-specific and family-environmental factors associated with these profiles and to explore links to everyday-life outcomes. Data from eight EF tests assessing working memory, inhibition, cognitive flexibility, switching, and planning in = 529 children aged between 7 and 16 years was subjected into a latent profile analysis.
View Article and Find Full Text PDFActa Paediatr
January 2025
Paediatric Neurology and Neurorehabilitation Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Aim: Young people with childhood-onset motor disabilities face unique challenges in understanding and managing their condition. This study explored how they learnt about their condition.
Method: A descriptive qualitative study was conducted in 2023-2024 at a Swiss paediatric neurorehabilitation unit.
Health Inf Sci Syst
December 2025
Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan.
This study introduces a novel classification method to distinguish children with autism from typically developing children. We recruited 50 school-age children in Taiwan, including 44 boys and 6 girls aged 6 to 12 years, and asked them to draw patterns from a visual-motor integration test to collect data and train deep learning classification models. Ensemble learning was adopted to significantly improve the classification accuracy to 0.
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