4.7 Article Data Paper

Lower limb kinematic, kinetic, and EMG data from young healthy humans during walking at controlled speeds

期刊

SCIENTIFIC DATA
卷 8, 期 1, 页码 -

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41597-021-00881-3

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资金

  1. FundacAo para a Ciencia e Tecnologia (FCT) [2020.05711.BD]
  2. P2020 [POCI-01-0247-FEDER-039868]
  3. FCT [UIDB/04436/2020, UIDP/04436/2020]
  4. FEDER Funds through the COMPETE 2020-Programa Operacional Competitividade e InternacionalizacAo (POCI)
  5. Fundação para a Ciência e a Tecnologia [2020.05711.BD] Funding Source: FCT

向作者/读者索取更多资源

This study presents a complete dataset of lower limb kinematic, kinetic, and EMG data recorded from sixteen healthy participants at seven controlled speeds, aiming to assist in evaluating human locomotion conditions. The dataset includes raw and processed data, providing valuable information for validating biomechanical gait models and serving as a reference trajectory for personalized control of robotic assistive devices.
Understanding the lower limb kinematic, kinetic, and electromyography (EMG) data interrelation in controlled speeds is challenging for fully assessing human locomotion conditions. This paper provides a complete dataset with the above-mentioned raw and processed data simultaneously recorded for sixteen healthy participants walking on a 10 meter-flat surface at seven controlled speeds (1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0km/h). The raw data include 3D joint trajectories of 24 retro-reflective markers, ground reaction forces (GRF), force plate moments, center of pressures, and EMG signals from Tibialis Anterior, Gastrocnemius Lateralis, Biceps Femoris, and Vastus Lateralis. The processed data present gait cycle-normalized data including filtered EMG signals and their envelope, 3D GRF, joint angles, and torques. This study details the experimental setup and presents a brief validation of the data quality. The presented dataset may contribute to (i) validate and enhance human biomechanical gait models, and (ii) serve as a reference trajectory for personalized control of robotic assistive devices, aiming an adequate assistance level adjusted to the gait speed and user's anthropometry.

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