Integrating contrastive cross-modal attention and stacked GRU for hand function rehabilitation robot control
Wei Liu, Huidong Wu, Shi-Fu Feng, Chang-Liang Luo
TL;DR
This paper introduces a new model for hand rehabilitation robots that improves control accuracy by combining attention mechanisms and recurrent neural networks.
Contribution
The novel C-GAP model integrates cross-modal attention and stacked GRUs for improved hand movement control in rehabilitation robots.
Findings
The C-GAP model effectively captures dynamic temporal features of hand movements using stacked GRUs.
The model performs stably on both healthy and stroke patient datasets, adapting to different hand function states.
It provides a complete control scheme for rehabilitation robots with adaptive PID optimization.
Abstract
With the intensification of population aging and the increasing incidence of neurological diseases, the demand for precise and intelligent control technology in hand rehabilitation robots has become more urgent. Traditional control methods struggle to effectively capture the dynamic temporal features of hand movements, especially in scenarios where there are modal differences between hand function data of healthy individuals and stroke patients, leading to insufficient control accuracy and poor generalization. This paper focuses on hand rehabilitation robot control technology and proposes the C-GAP model. By designing a cross-modal attention mechanism, the model realizes feature collaboration of multi-source data such as electromyography (EMG), force, and joint angles. It relies on Stacked Gated Recurrent Units to accurately extract the temporal dynamic patterns of typical hand…
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Taxonomy
TopicsStroke Rehabilitation and Recovery · Motor Control and Adaptation · Muscle activation and electromyography studies