AI & ML
Does Haptic Connection to a Healthy Partner Improve Motor Learning in Chronic Stroke? A Pilot Study of Robot-Based Dyadic Training
arXiv cs.RO0 views
arXiv:2611.13307v1 Announce Type: new Abstract: Purpose: Robot-based haptic dyads, where two users exchange forces through robots, have been proposed for robot-assisted neurorehabilitation. This intervention could increase motor learning by promoting motor variability, providing an implicit communication channel, and facilitating social interaction. Despite the potential benefits, few studies have investigated robot-based haptic dyads for neurological populations, and none have included a comprehensive clinical assessment. Design/Methodology/Approach: We present a pilot study investigation of individuals with chronic stroke learning a complex tracking task in solo and dyad conditions. Prior to training, participants completed a battery of cognitive, motor, and sensory assessments. We assessed changes in key kinematic outcomes including tracking error, distance traveled, mean absolute power, and sample entropy. We also performed a correlation analysis of clinical scores with baseline tracking errors. Findings: Baseline tracking error was strongly correlated (r > 0.6) with lower cognitive scores and higher motor scores. Only the solo group demonstrated lower tracking error at assessment ({\Delta} = -5.33, 95%CI [-10.25, -0.40], p = 0.027), but this result was tempered by the removal of an outlier participant. Neither group demonstrated clear retention. The dyad group showed significantly elevated performance, effort, and movement complexity during training. Research Limitations/Implications: The selected task was too difficult to reveal learning for either group, which may be related to its high cognitive demand. Sample size and stroke heterogeneity were also limitation. Originality/Value: Dyadic training increased movement complexity and effort, which are associated with better motor learning. Clinical characteristics such as lesion location, cognition, sensation, and chronicity may influence potential benefits of dyadic training.
Source:https://arxiv.org/abs/2611.13307PreviousLarge Language Model Explainability for Planning Lunar Rover Trajectories under UncertaintyNextPneuGen: Task-Driven Agentic Co-Design of Soft Pneumatic Robots
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