ArticleScientific data2021
Gutenberg Gait Database, a ground reaction force database of level overground walking in healthy individuals.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 42 citations in OpenAlex.
- Dual-Model Artificial Intelligence Framework Integrating AI-Based Markerless Motion Capture for Dynamic Gait Prediction and Health-Status Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Associations between knee extensor strength and gait characteristics derived from shoe-based wearable sensors in older women.Scientific reports · 2026Article
- The effects of different types of dual-task gait training on lower-limb coordination during dual-task gait in patients with stroke: a single-blind randomized controlled trial.BMC sports science, medicine & rehabilitation · 2026Article
- Whole-body optical marker and ground reaction force data of healthy humans performing non-cyclic activities.Scientific data · 2025Article
- A dataset of high-resolution plantar pressures for gait analysis across varying footwear and walking speeds.Scientific data · 2025Article
- A Gait Sub-Phase Switching-Based Active Training Control Strategy and Its Application in a Novel Rehabilitation Robot.Biosensors · 2025Article
- Modelling individual variation in human walking gait across populations and walking conditions via gait recognition.Journal of the Royal Society, Interface · 2024Article
- A Preliminary Study on Kinetic Analysis of Ground Reaction Force and Impulse During Gait in Patients With Total Hip Replacement and Implication for Rehabilitation.Orthopaedic surgery · 2024Observational
- A Biomechanical Dataset of 1,798 Healthy and Injured Subjects During Treadmill Walking and Running.Scientific data · 2024Article
- Quantifying walking speeds in relation to ankle biomechanics on a real-time interactive gait platform: a musculoskeletal modeling approach in healthy adults.Frontiers in bioengineering and biotechnology · 2024Article
- Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence.Frontiers in bioengineering and biotechnology · 2024Article
- Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data.Sensors (Basel, Switzerland) · 2023Article
- Real-world measurements of ground reaction forces of normal gait of young adults wearing various footwear.Scientific data · 2023Article
- Modeling biological individuality using machine learning: A study on human gait.Computational and structural biotechnology journal · 2023Article
- Article
- A Multi-Modal Gait Database of Natural Everyday-Walk in an Urban Environment.Scientific data · 2022Article
- Always Pay Attention to Which Model of Motor Learning You Are Using.International journal of environmental research and public health · 2022Review
- Gutenberg Gait Database, a ground reaction force database of level overground walking in healthy individuals.Scientific data · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.