health life sciences medicine
Gait is a fundamental motor function, and its analysis is essential for understanding locomotor control, rehabilitation, and the early detection of neurological and musculoskeletal disorders. While many datasets capture either biomechanical or neural aspects of gait, publicly available multimodal datasets that integrate brain, muscle, kinematic, and ground reaction force recordings remain scarce. This limitation restricts advances in modeling neuromechanical interactions and the development of machine learning approaches for gait classification and rehabilitation technologies. To address this gap, we provide a comprehensive dataset of treadmill walking from 59 healthy adults with a mean age of 24 ± 5 years, representing both sexes and different body mass index categories. Participants walked at three controlled speeds (0.5, 0.75, and 1.0 m/s), with synchronized recordings from scalp electroencephalography, surface electromyography of 12 lower-limb muscles, inertial sensors capturing kinematics, and bilateral force plates measuring three-dimensional forces, moments, and center of pressure. The dataset enables investigations into brain-body interactions, speed-dependent adaptations, and neuromechanical variability, while supporting the benchmarking of computational models for gait analysis.
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Creative Commons Attribution 4.0 International Public License
https://doi.org/10.13026/r0ea-7161
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A multimodal gait dataset of brain activity, muscle activity, kinematics and ground forces in young adults was accessed on DATE from https://registry.opendata.aws/multimodal-gait-dataset.
arn:aws:s3:::physionet-open/multimodal-gait-dataset/us-east-1aws s3 ls --no-sign-request s3://physionet-open/multimodal-gait-dataset/