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.

Download full-text PDF

Source
http://dx.doi.org/10.1109/TOH.2024.3379035DOI Listing

Publication Analysis

Top Keywords

motor learning
24
motor
9
robot-based haptic
8
haptic dyads
8
interaction characteristics
8
learning
6
haptic
5
learning robot-based
4
dyads review
4
review rehabilitation
4

Similar Publications

A comparison of force adaptation in toddlers and adults during a drawer opening task.

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 PDF

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 PDF

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 PDF

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.

View Article and Find Full Text PDF

Detecting autism in children through drawing characteristics using the visual-motor integration test.

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.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!