ArticleScientific data2023
High-density EMG, IMU, kinetic, and kinematic open-source data for comprehensive locomotion activities.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- A multimodal EMG-IMU dataset and multi-dataset benchmark for deep learning-based human activity recognition.Scientific reports · 2026Article
- A Wearable Motion Capture Dataset for Gait Analysis Using IMUs and Shank-Mounted Egocentric Cameras.Scientific data · 2026Article
- A multimodal EMG and IMU dataset for assessing the quality of exercises designed for spatially constrained environments.Data in brief · 2026Article
- A Full-Body Motion Capture Gait Dataset of Healthy Young Adults Walking Ramps Up and Down.Scientific data · 2026Article
- Whole-body optical marker and ground reaction force data of healthy humans performing non-cyclic activities.Scientific data · 2025Article
- A Dataset of Clinical Gait Signals with Wearable Sensors from Healthy, Neurological, and Orthopedic Cohorts.Scientific data · 2025Article
- Comprehensive human locomotion and electromyography dataset: Gait120.Scientific data · 2025Article
- A Knee Rehabilitation Exercises Dataset for Postural Assessment using Wearable Devices.Scientific data · 2025Article
- Learning based lower limb joint kinematic estimation using open source IMU data.Scientific reports · 2025Article
- 3D motion analysis dataset of healthy young adult volunteers walking and running on overground and treadmill.Scientific data · 2024Article
- High-density EMG, IMU, kinetic, and kinematic open-source data for comprehensive locomotion activities.Scientific data · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Novel sensor technology enables new insights in the neuromechanics of human locomotion that were previously not possible. Here, we provide a dataset of high-density surface electromyography (HDsEMG) and high-resolution inertial measurement unit (IMU) signals, along with motion capture and force data for the lower limb of 10 healthy adults during multiple locomotion modes. The participants performed level-ground and slope walking, as well as stairs ascent/descent, side stepping gait, and stand-to-walk and sit-to-stand-to-walk, at multiple walking speeds. These data can be used for the development and validation of locomotion mode recognition and control algorithms for prosthetics, exoskeletons, and bipedal robots, and for motor control investigations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.