Human Leg Kinematics, Kinetics, and EMG during Phase-Shifting Perturbations at Varying Inclines

Citation Author(s):
Submitted by:
Rebecca Macaluso
Last updated:
Thu, 07/16/2020 - 16:30
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This dataset contains joint kinematics, kinetics, and EMG activity from an experimental protocol approved by the Institutional Review Board at the University of Texas at Dallas. This data was collected to evaluate the robustness of different parameterization variables during perturbations for application in robotic prosthetic legs. Ten able-bodied subjects self-selected a comfortable speed for walking on level (0 degree), +5 degree, and -5 degree inclines. Subjects walked at the self-selected speed for a minute without perturbations to produce a control dataset of unperturbed kinematics. From this control data, the average stance time was calculated for each subject to define a normalized time window between 0 and 80% of stance. Then, 100 uniformly distributed times were sampled from this window to determine the perturbation onset times, i.e., the amount of delay between heel strike and perturbation onset. For perturbation trials, subjects walked at the same speed for 20-25 minutes, broken into 5 sets of 4-5 minutes for each slope. During each trial, a 10-camera Vicon motion capture system recorded leg kinematics, while force plates in a Bertec split-belt treadmill recorded ground reaction forces, and a Delsys Trigno EMG system recorded muscle activation of the rectus femoris, biceps femoris, tibialis anterior, and gastrocnemius.



R. Macaluso, K. Embry, D. Villarreal, R.D. Gregg, “Parameterizing Human Locomotion Across Quasi-Random Treadmill Perturbations and Inclines,” IEEE Transactions on Neural Systems and Rehabilitation Engineering


Please see the README document for:

  • Details on the available data, how it was collected, and how it has been processed
  • An example of how to efficiently traverse the dataset (ExampleScript.m)
  • Instructions for the script used to execute the experiment (Treadmill_Perturbation_Shell.m)


good work

Submitted by li zhengyi on Mon, 08/10/2020 - 04:28

Thanks for such a rich dataset.

Submitted by Muhammad Huzaifa on Wed, 01/20/2021 - 21:56