Abstract
In a typical clinical gait analysis, the gait patterns of pathological individuals are commonly compared with the typically faster, comfortable pace of healthy subjects. However, due to potential bias related to gait speed, this comparison may not be valid. Publicly available gait datasets have failed to address this issue. Therefore, the goal of this study was to present a publicly available dataset of 42 healthy volunteers (24 young adults and 18 older adults) who walked both overground and on a treadmill at a range of gait speeds. Their lower-extremity and pelvis kinematics were measured using a three-dimensional (3D) motion-capture system. The external forces during both overground and treadmill walking were collected using force plates and an instrumented treadmill, respectively. The results include both raw and processed kinematic and kinetic data in different file formats: c3d and ASCII files. In addition, a metadata file is provided that contain demographic and anthropometric data and data related to each file in the dataset. All data are available at Figshare (DOI: 10.6084/m9.figshare.5722711). We foresee several applications of this public dataset, including to examine the influences of speed, age, and environment (overground vs. treadmill) on gait biomechanics, to meet educational needs, and, with the inclusion of additional participants, to use as a normative dataset.
Highlights
Gait analysis (GA) has been widely used to better understand the gait patterns of a wide range of populations
The total number of gait trials is not the same across participants because it reflects the variation in the number of valid trials per participant
This study presents a dataset of treadmill and overground walking kinematics and kinetics in a range of gait speeds for 24 healthy young individuals and 18 healthy older individuals
Summary
Gait analysis (GA) has been widely used to better understand the gait patterns of a wide range of populations The application of this method has the ability to distinguish between normal and abnormal gaits (Gage et al, 2009), to determine the best intervention (Kay et al, 2000; Lofterod et al, 2007; Wren et al, 2011), and to detect pathologies at subclinical stages (Carpinella et al, 2007; Rao et al, 2008). These measures are objective and are typically performed using a three-dimensional (3D) motion-capture system and force plates.
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