Analysis of Fatigue-Induced Compensatory Movements in Bicep Curls: Gaining Insights for the Deployment of Wearable Sensors

IF 3.4 Q2 ENGINEERING, BIOMEDICAL IEEE transactions on medical robotics and bionics Pub Date : 2024-03-30 DOI:10.1109/TMRB.2024.3407239
Ming Xuan Chua;Yoshiro Okubo;Shuhua Peng;Thanh Nho Do;Chun Hui Wang;Liao Wu
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Abstract

A common challenge in Bicep Curls rehabilitation is muscle compensation, where patients adopt alternative movement patterns when the primary muscle group cannot act due to injury or fatigue, significantly decreasing the effectiveness of rehabilitation efforts. The problem is exacerbated by the growing trend toward transitioning from in-clinic to home-based rehabilitation, where constant monitoring and correction by physiotherapists are limited. Developing wearable sensors capable of detecting muscle compensation becomes crucial to address this challenge. This study aims to gain insights into the optimal deployment of wearable sensors through a comprehensive study of muscle compensation in Bicep Curls. We collect upper limb joint kinematics and surface electromyography signals (sEMG) from eight muscles in 12 healthy subjects during standard and fatigue stages. Two muscle synergies are derived from sEMG signals and are analyzed comprehensively along with joint kinematics. Our findings reveal a shift in the relative contribution of forearm muscles to shoulder muscles, accompanied by a significant increase in activation amplitude for both synergies. Additionally, more pronounced movement was observed at the shoulder joint during fatigue. These results suggest focusing on the shoulder muscle activities and joint motions when deploying wearable sensors to effectively detect compensatory movements.
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二头肌弯举中疲劳诱发的补偿动作分析:深入了解可穿戴传感器的部署情况
二头肌弯举康复中的一个常见挑战是肌肉代偿,即当主要肌群因受伤或疲劳而无法运动时,患者会采用其他运动模式,从而大大降低了康复效果。从门诊康复过渡到家庭康复的趋势日益明显,理疗师的持续监测和纠正受到限制,这就加剧了这一问题。开发能够检测肌肉代偿的可穿戴传感器对于应对这一挑战至关重要。本研究旨在通过对二头肌弯举中肌肉代偿的全面研究,深入了解可穿戴传感器的最佳部署。我们收集了 12 名健康受试者在标准和疲劳阶段八块肌肉的上肢关节运动学和表面肌电信号(sEMG)。我们从肌电图信号中得出两组肌肉的协同作用,并与关节运动学数据一起进行了综合分析。我们的研究结果表明,前臂肌肉对肩部肌肉的相对贡献发生了变化,同时两种协同作用的激活幅度也显著增加。此外,疲劳时肩关节的运动更为明显。这些结果表明,在部署可穿戴传感器以有效检测代偿运动时,应重点关注肩部肌肉活动和关节运动。
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Table of Contents IEEE Transactions on Medical Robotics and Bionics Society Information Guest Editorial Special section on the Hamlyn Symposium 2023—Immersive Tech: The Future of Medicine IEEE Transactions on Medical Robotics and Bionics Publication Information IEEE Transactions on Medical Robotics and Bionics Information for Authors
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