ArticleJournal of robotic surgery2025
Surface electromyography markers of motor learning during laparoscopic skill acquisition: associations with performance and movement efficiency.
Article in Journal of robotic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
Objective assessment of motor learning is critical for tracking surgical skill acquisition and optimizing feedback. Surface electromyography (sEMG) provides a non-invasive method to monitor neuromuscular activity that reflects performance efficiency. This study examined the associations between sEMG features during laparoscopic task practice and both task performance and hand movement efficiency. Seven participants performed a multi-membrane needle transfer task on the LAP-X hybrid simulator. Five participants completed four attempts and two completed seven attempts. sEMG signals were recorded bilaterally from deltoid, trapezius, biceps brachii, brachioradialis, and flexor carpi radialis muscles. Performance was measured using a composite drops/errors/collisions score and hand trajectory lengths. Repeated-measures correlation analysis was used to examine associations between sEMG features and performance metrics. Benjamini-Hochberg false discovery rate (FDR) correction was applied to account for multiple comparisons. Several sEMG features-including root mean square (RMS), mean absolute value (MAV), zero crossing count (ZC), mean frequency (MNF), and median frequency (MDF)-were significantly correlated with performance outcomes. Higher complexity and frequency in biceps activity (i.e., higher ZC and MNF) were associated with better performance and shorter movement trajectories. Increased RMS in brachioradialis was associated with improved task performance. However, greater activation in the trapezius (e.g., higher RMS, MAV, and MDF) was linked to longer trajectories and more drops/errors, suggesting compensatory motor strategies. These findings support the use of sEMG-derived muscle activation patterns as objective markers of motor learning. These results characterize muscle-specific contributions to performance and may support the development of adaptive, physiologically based feedback strategies to enhance laparoscopic training.
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