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

Abstract

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 4.0 km/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.

Kinematics, Speed, and Anthropometry-Based Ankle Joint Torque Estimation: A Deep Learning Regression Approach

“…The Newington-Hayes marker-set was adopted to determine the kinematic data from the lower limb joints [47]. A total of 12 pairs of retro-reflective markers were used, as presented in Figure 1.…”

“…The 3D marker trajectories registered with the motion-capture system were filtered by the same filter, using a cutoff frequency of 6 Hz [47]. Hereinafter, the filtered 3D marker trajectories, along with the GRF data, were processed in Visual3D software to compute the lower limb joint angles and torques.…”

Analysis and Recognition of Human Lower Limb Motions Based on Electromyography (EMG) Signals

“…The results are given in Tables 1 and 2. According to the principles of mathematical statistics, if there is a high positive correlation between muscles, this indicates that the length of the two muscles tends to be consistent in the gait process; that is, the role of the two muscles in the flexion and extension of the joint is the same [13]. For example, in terms of the lateral femoral muscle and the medial femoral muscle, their function is to extend the knee, and their position on the thigh is similar, thus their correlation is 1, as with the lateral head and medial head of the gastrocnemius.…”

“…For example, in terms of the lateral femoral muscle and the medial femoral muscle, their function is to extend the knee, and their position on the thigh is similar, thus their correlation is 1, as with the lateral head and medial head of the gastrocnemius. The highly negative correlation between the muscles indicates that the length changes of the two muscles tend to be opposite in gait motion, that is, they play opposite roles in joint flexion and extension [13]. For example, the anterior tibialis and gastrocnemius are used for plantar and dorsal flexion of the ankle, so their correlation was −0.879.…”

“…This paper's structure is shown in Figure 1. In the existing research on lower limb movement classification by EMG signals, when concluding the experiment, the methods used for selecting muscles are mostly based on the distribution position of human muscles or on the convenience of collection by re-searchers [12][13][14]. Although the results are still objective, they lack a certain scientific basis.…”