Review highlights advances in multimodal information fusion for enhancing rehabilitation in motor dysfunction, suggesting implications for device optimization.
Key Points
This review explores multimodal information fusion control techniques in assistive devices aimed at improving rehabilitation for motor dysfunction.
Reviewed recent literature on multimodal information fusion in rehabilitation robots.
Analyzed advantages and disadvantages of various fusion levels: data-level, feature-level, and decision-level.
Evaluated commonly used fusion algorithms in the context of rehabilitation equipment design.
Presented insights into how information fusion enhances rehabilitation efficacy and specificity.
Identified key challenges and benefits associated with different levels of data fusion.
Provided a comprehensive overview of the current state of research in assistive devices for motor dysfunction.