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. To address this challenge, developing wearable sensors capable of detecting muscle compensation becomes crucial. This study aims to gain insights for 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 should muscle activities and joint motions when deploying wearable sensors for effective detection of compensatory movements.

翻译：二头肌弯举康复中的一个常见挑战是肌肉代偿，即当主要肌群因受伤或疲劳无法正常作用时，患者会采用替代运动模式，从而显著降低康复效果。随着康复方式从临床环境向家庭康复转变，物理治疗师的持续监测与纠正常常受限，这一问题进一步加剧。为应对这一挑战，开发能够检测肌肉代偿的可穿戴传感器至关重要。本研究旨在通过全面分析二头肌弯举中的肌肉代偿现象，为可穿戴传感器的最优部署提供依据。我们收集了12名健康受试者在标准阶段和疲劳阶段的上肢关节运动学数据及八块肌肉的表面肌电信号（sEMG）。从sEMG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。研究结果揭示，在疲劳阶段，前臂肌肉与肩部肌肉的相对贡献发生转变，两种协同模式的激活幅度均显著增加。此外，疲劳时肩关节的运动幅度更为明显。这些结果表明，在部署可穿戴传感器以有效检测代偿动作时，应重点关注肩部肌肉活动和关节运动。