Wearable sensors have recently been extensively used in sports science, physical rehabilitation, and industry providing feedback on physical fatigue. Information obtained from wearable sensors can be analyzed by predictive analytics methods, such as machine learning algorithms, to determine fatigue during shoulder joint movements, which have complex biomechanics. The presented dataset aims to provide data collected via wearable sensors during a fatigue protocol involving dynamic shoulder internal rotation (IR) and external rotation (ER) movements. Thirty-four healthy subjects performed shoulder IR and ER movements with different percentages of maximal voluntary isometric contraction (MVIC) force until they reached the maximal exertion. The dataset includes demographic information, anthropometric measurements, MVIC force measurements, and digital data captured via surface electromyography, inertial measurement unit, and photoplethysmography, as well as self-reported assessments using the Borg rating scale of perceived exertion and the Karolinska sleepiness scale. This comprehensive dataset provides valuable insights into physical fatigue assessment, allowing the development of fatigue detection/prediction algorithms and the study of human biomechanical characteristics during shoulder movements within a fatigue protocol.

可穿戴传感器最近广泛应用于运动科学、物理康复和提供身体疲劳反馈的工业领域。从可穿戴传感器获得的信息可以通过预测分析方法（例如机器学习算法）进行分析，以确定具有复杂生物力学的肩关节运动期间的疲劳程度。所提出的数据集旨在提供在涉及动态肩部内旋（IR）和外旋（ER）运动的疲劳协议期间通过可穿戴传感器收集的数据。 34 名健康受试者以不同百分比的最大自主等长收缩 (MVIC) 力进行肩部 IR 和 ER 运动，直至达到最大用力。该数据集包括人口统计信息、人体测量值、MVIC 力测量值、通过表面肌电图、惯性测量单元和光电体积描记法捕获的数字数据，以及使用 Borg 感知用力评分量表和 Karolinska 嗜睡量表进行的自我报告评估。这个全面的数据集为身体疲劳评估提供了宝贵的见解，允许开发疲劳检测/预测算法以及研究疲劳协议中肩部运动期间的人体生物力学特征。