The natural interaction and control performance of lower limb rehabilitation robots are closely linked to biomechanical information from various human locomotion activities. Multidimensional human motion data significantly deepen the understanding of the complex mechanisms governing neuromuscular alterations, thereby facilitating the development and application of rehabilitation robots in multifaceted real-world environments. However, existing lower limb datasets are inadequate for supplying the essential multimodal data and large-scale gait samples necessary for the development of effective data-driven approaches, and the significant effects of acquisition interference in real applications are neglected. To fill this gap, we present the K2MUSE dataset, which includes a comprehensive collection of multimodal data, comprising kinematic, kinetic, amplitude mode ultrasound (AUS), and surface electromyography (sEMG) measurements. The proposed dataset includes lower-limb multimodal data collected from two cohorts, including 30 able-bodied young adults and 12 older adults, across different inclines (0$^\circ$, $\pm$5$^\circ$, and $\pm$10$^\circ$), speeds (0.5 m/s, 1.0 m/s, and 1.5 m/s), and representative non-ideal acquisition conditions (muscle fatigue, electrode shifts, and interday differences). The kinematic and ground reaction force data were collected with a Vicon motion capture system and an instrumented treadmill with embedded force plates, whereas the sEMG and AUS data of thirteen muscles on the bilateral lower limbs were synchronously recorded. K2MUSE is released with the corresponding structured documentation, preprocessing pipelines, and example code, thereby providing a comprehensive resource for rehabilitation robot development, biomechanical analysis, and wearable sensing research. The dataset is available at https://k2muse.github.io/.

翻译：下肢康复机器人的自然交互与控制性能与人体多种运动活动的生物力学信息密切相关。多维人体运动数据显著加深了对神经肌肉改变复杂机制的理解，从而有助于在多面现实环境中推动康复机器人的开发与应用。然而，现有下肢数据集不足以提供开发有效数据驱动方法所需的关键多模态数据和大规模步态样本，且忽视了实际应用中采集干扰的显著影响。为弥补这一空白，我们提出了K2MUSE数据集，该数据集包含全面的多模态数据，涵盖运动学、动力学、幅度模式超声（AUS）和表面肌电（sEMG）测量。所提出的数据集包括来自两个群组（30名健全年轻成年人和12名老年人）的下肢多模态数据，采集于不同坡度（0$^\circ$，$\pm$5$^\circ$，$\pm$10$^\circ$）、速度（0.5 m/s，1.0 m/s，1.5 m/s）以及代表性非理想采集条件（肌肉疲劳、电极移位和日间差异）。运动学和地面反作用力数据通过Vicon运动捕捉系统和内置测力板的仪器化跑台采集，而双侧下肢十三块肌肉的sEMG和AUS数据则同步记录。K2MUSE随附相应的结构化文档、预处理流程和示例代码发布，从而为康复机器人开发、生物力学分析和可穿戴传感研究提供全面资源。数据集可在https://k2muse.github.io/获取。