Abstract
The Gutenberg Gait Database comprises data of 350 healthy individuals recorded in our laboratory over the past seven years. The database contains ground reaction force (GRF) and center of pressure (COP) data of two consecutive steps measured - by two force plates embedded in the ground - during level overground walking at self-selected walking speed. The database includes participants of varying ages, from 11 to 64 years. For each participant, up to eight gait analysis sessions were recorded, with each session comprising at least eight gait trials. The database provides unprocessed (raw) and processed (ready-to-use) data, including three-dimensional GRF and two-dimensional COP signals during the stance phase. These data records offer new possibilities for future studies on human gait, e.g., the application as a reference set for the analysis of pathological gait patterns, or for automatic classification using machine learning. In the future, the database will be expanded continuously to obtain an even larger and well-balanced database with respect to age, sex, and other gait-specific factors.
Highlights
Background & SummaryThe ability to walk is crucial for human mobility and is closely related to quality of life independent of age and sex[1,2,3,4]
Thereby, we aim at creating a basis for reliable machine learning (ML) models that can be used as decision-support system in clinical practice and research
Kinematic and electromyographic data are prone to several difficulties, such as inconsistencies due to differences in anthropometric characteristics of participants, experience of investigators, measurement protocols, and laboratory settings[42,43,44]
Summary
The ability to walk is crucial for human mobility and is closely related to quality of life independent of age and sex[1,2,3,4]. Thereby, we aim at creating a basis for reliable ML models that can be used as decision-support system in clinical practice and research Based on this goal, we prepared the processed data in such a way that it can be merged and used in conjunction with the GaitRec dataset[35]. Kinematic and electromyographic data are prone to several difficulties, such as inconsistencies due to differences in anthropometric characteristics of participants, experience of investigators, measurement protocols, and laboratory settings[42,43,44] This makes it more difficult to create a homogeneous, large-scale, and high-quality dataset compared to using less interference-prone data, such as GRF signals[45,46]. Previous studies[23,47] investigating ML methods for automated classification of gait impairments based on force plate data showed promising results suggesting their suitability for clinical applications
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
