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.

翻译：二头肌弯举康复中的一个常见挑战是肌肉代偿，即当主要肌群因损伤或疲劳无法正常工作时，患者会采用替代性运动模式，这显著降低了康复效果。随着康复从临床向家庭过渡的趋势日益增长，物理治疗师的持续监测和纠正受到限制，使得该问题更加突出。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，为可穿戴传感器的优化部署提供见解。我们采集了12名健康受试者在标准状态和疲劳状态下进行二头肌弯举时的上肢关节运动学数据以及来自八块肌肉的表面肌电信号。从表面肌电信号中提取出两种肌肉协同模式，并结合关节运动学进行了综合分析。研究结果表明，前臂肌肉相对于肩部肌肉的贡献度发生转移，同时两种协同模式的激活幅度均显著增加。此外，在疲劳期间观察到肩关节运动更为明显。这些结果提示，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。