The biomechanics of human walking are well documented for standard conditions such as for self-selected step length and preferred speed. However, humans can and do walk with a variety of other step lengths and speeds during daily living. The variation of biomechanics across gait conditions may be important for describing and determining the mechanics of locomotion. To address this, we present an open biomechanics dataset of steady walking at a broad range of conditions, including 33 experimentally-controlled combinations of speed (0.7-2.0 m·s), step length (0.5-1.1 m), and step width (0-0.4 m). The dataset contains ground reaction forces and motions from healthy young adults (N = 10), collected using split-belt instrumented treadmill and motion capture systems respectively. Most trials also include pre-computed inverse dynamics, including 3D joint positions, angles, torques and powers, as well as intersegmental forces. Apart from raw data, we also provide five strides of good quality data without artifacts for each trial, and sample software for visualization and analysis.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669008 | PMC |
| http://dx.doi.org/10.1038/s41597-022-01817-1 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
EMG Dataset for Gesture Recognition with Arm Translation.
Sci Data
January 2025
School of Informatics, The University of Edinburgh, Edinburgh, EH8 9AB, United Kingdom.
Myoelectric control has emerged as a promising approach for a wide range of applications, including controlling limb prosthetics, teleoperating robots and enabling immersive interactions in the Metaverse. However, the accuracy and robustness of myoelectric control systems are often affected by various factors, including muscle fatigue, perspiration, drifts in electrode positions and changes in arm position. The latter has received less attention despite its significant impact on signal quality and decoding accuracy.
View Article and Find Full Text PDFA data-driven framework for developing a unified density-modulus relationship for the human lumbar vertebral body.
J Mech Behav Biomed Mater
January 2025
Department of Mechanical Engineering, Boston University, Boston, MA 02215, USA; Center for Multiscale and Translational Mechanobiology, Boston University, Boston, MA 02215, USA; Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.
Despite the broad agreement that bone stiffness is heavily dependent on the underlying bone density, there is no consensus on a unified relationship that applies to both cancellous and cortical compartments. Bone from the two compartments is generally assessed separately, and few mechanical test data are available for samples from the transitional regions between them. In this study, we present a data-driven framework integrating experimental testing and numerical modeling of the human lumbar vertebra through an energy balance criterion, to develop a unified density-modulus relationship across the entire vertebral body, without the necessity of differentiation between trabecular and cortical regions.
View Article and Find Full Text PDFDataset of running kinematics, kinetics and muscle activation at different speeds, surface gradients, cadences and with forward trunk lean.
Data Brief
June 2024
NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Department of Nutrition and Movement Sciences, Maastricht, the Netherlands.
Data Collection Process: This dataset includes running biomechanics measured using an instrumented treadmill combined with three- dimensional motion capture and surface muscle activation among 19 healthy participants (10 males, 9 females, mean ± SD age 23.6 ± 3.7 years, body height 174.
View Article and Find Full Text PDFKinematics, kinetics, and muscle activations during human locomotion over compliant terrains.
Sci Data
January 2025
University of Delaware, Department of Mechanical Engineering, Newark, DE, 19716, USA.
Walking on compliant terrains, like carpets, grass, and soil, presents a unique challenge, especially for individuals with mobility impairments. In contrast to rigid-ground walking, compliant surfaces alter movement dynamics and increase the risk of falls. Understanding and modeling gait control across such soft and deformable surfaces is thus crucial for maintaining daily mobility.
View Article and Find Full Text PDFGround Reaction Forces and Joint Moments Predict Metabolic Cost in Physical Performance: Harnessing the Power of Artificial Neural Networks.
Appl Sci (Basel)
June 2024
Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE 68182, USA.
Understanding metabolic cost through biomechanical data, including ground reaction forces (GRFs) and joint moments, is vital for health, sports, and rehabilitation. The long stabilization time (2-5 min) of indirect calorimetry poses challenges in prolonged tests. This study investigated using artificial neural networks (ANNs) to predict metabolic costs from the GRF and joint moment time series.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!