Shafiei Somayeh B, Shadpour Saeed, Toussi Mehdi Seilanian
Department of Urology, Intelligent Cancer Care Laboratory, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, NY, 14263, USA.
Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada.
J Robot Surg. 2025 Jun 28;19(1):330. doi: 10.1007/s11701-025-02505-z.
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.
运动学习的客观评估对于追踪手术技能的获得以及优化反馈至关重要。表面肌电图（sEMG）提供了一种非侵入性方法来监测反映操作效率的神经肌肉活动。本研究考察了腹腔镜任务练习期间sEMG特征与任务表现和手部运动效率之间的关联。七名参与者在LAP-X混合模拟器上执行了一项多膜针转移任务。五名参与者完成了四次尝试，两名参与者完成了七次尝试。从三角肌、斜方肌、肱二头肌、肱桡肌和桡侧腕屈肌双侧记录sEMG信号。使用综合掉落/错误/碰撞得分和手部轨迹长度来衡量表现。采用重复测量相关分析来考察sEMG特征与表现指标之间的关联。应用Benjamini-Hochberg错误发现率（FDR）校正来处理多重比较。几个sEMG特征，包括均方根（RMS）、平均绝对值（MAV）、过零计数（ZC）、平均频率（MNF）和中位数频率（MDF），与表现结果显著相关。肱二头肌活动中更高的复杂性和频率（即更高的ZC和MNF）与更好的表现和更短的运动轨迹相关。肱桡肌中RMS的增加与任务表现的改善相关。然而，斜方肌中更大的激活（例如，更高的RMS、MAV和MDF）与更长的轨迹以及更多的掉落/错误相关，提示代偿性运动策略。这些发现支持将sEMG衍生的肌肉激活模式用作运动学习的客观标志物。这些结果表征了肌肉对表现的特异性贡献，并可能支持开发基于生理的适应性反馈策略以加强腹腔镜训练。
