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.
Highlights
Background & SummaryWalking appears to be the most performed human motor task[1]
Healthy human gait may be compromised by neurological diseases[2,3]
We present a multimodal walking dataset containing EMG data from four muscles (Tibialis Anterior (TA), Gastrocnemius Lateralis (GAL), Biceps Femoris (BF), and Vastus Lateralis (VL)), 3D ground reaction forces (GRFs), Center of Pressures (CoPs), and joint angles and torques of the lower limb joints along with the pelvis segment
Summary
Walking appears to be the most performed human motor task[1]. healthy human gait may be compromised by neurological diseases (such as stroke and spinal cord injury)[2,3]. Robotic devices endow control architectures that require reference trajectories (e.g., joint angles, torques, or muscle activations) to determine the amount of needed assistance or to define a target walking pattern[4] These reference trajectories are commonly obtained from public walking datasets from healthy users’ self-selected speeds. The proposed multimodal walking dataset may contribute to (i) the development of biomechanical gait models scaled to variable anthropometric parameters and speeds; (ii) assess the human locomotion conditions based on lower limb kinematic, kinetic, and EMG interrelation at controlled speeds; (iii) perform a biomechanical analysis of gait and muscle activation patterns during speed variation; and (iv) serve as reference trajectories for robotic devices, aiming an adequate assistance level adjusted to the speed and user’s anthropometry
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