K2MUSE: A human lower-limb multimodal walking dataset spanning task and acquisition variability for rehabilitation robotics

Abstract

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°, ±5°, and ±10°), 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 13 muscles on the bilateral lower limbs were synchronously recorded. To validate the quality of the data, we quantified repeatability across locomotion modes, speeds, and inclines, examined physiological signatures under non-ideal acquisition conditions, and observed high agreement with existing public datasets. We also report baseline motion-intention recognition results, including joint angle estimation and gait phase classification, with a multimodal transformer model demonstrating accurate and stable performance. In addition, we present a control-oriented application in which an end-to-end model trained on the K2MUSE dataset provides hip assistance via a soft exoskeleton and yields consistent reductions in metabolic cost across multiple terrains. 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/.