ArticleScientific data2023
Real-world measurements of ground reaction forces of normal gait of young adults wearing various footwear.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 15 citations in OpenAlex.
- Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026Review
- Preliminary Technical Feasibility of Integrating Auxetic Foam into Foot Orthoses for Diverse Neuropathic Etiologies: A Small-Scale Pilot Observation.Bioengineering (Basel, Switzerland) · 2026Article
- A Structural Vibration-based Dataset for Human Gait Recognition.Scientific data · 2025Article
- A dataset of high-resolution plantar pressures for gait analysis across varying footwear and walking speeds.Scientific data · 2025Article
- 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
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
For years, researchers have been recognizing patterns in gait for purposes of medical diagnostics, rehabilitation, and biometrics. A method for observing gait is to measure ground reaction forces (GRFs) between the foot and solid plate with tension sensors. The presented dataset consists of 13,702 measurements of bipedal GRFs of one step of normal gait of 324 students wearing shoes of various types. Each measurement includes raw digital signals of two force plates. A signal comprises stance-related samples but also preceding and following ones, in which one can observe noise, interferences, and artifacts caused by imperfections of devices and walkway. Such real-world time series can be used to study methods for detecting foot-strike and foot-off events, and for coping with artifacts. For user convenience, processed data are also available, which describe only the stance phase of gait and form ready-to-use patterns suitable for experiments in GRF-based recognition of persons and footwear, and for generating synthetic GRF waveforms. The dataset is accompanied by Matlab and Python programs for organizing and validating data.
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Registered trials
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.