ArticleScientific reports2021
Uniqueness of gait kinematics in a cohort study.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 26 citations in OpenAlex.
- The individuality of plantar pressure indices enables the reduction of descriptive gait data required for diagnostic purposes.Scientific reports · 2025Article
- Correction to at least neutral alignment during high tibial osteotomy is sufficient in reducing the knee adduction moment.Scientific reports · 2025Article
- Pattern analysis using lower body human walking data to identify the gaitprint.Computational and structural biotechnology journal · 2024Article
- Regional differences in the biological variability of plantar pressure as a basis for refining diagnostic gait analysis.Scientific reports · 2024Article
- Development and Validation of a Novel In Vitro Joint Testing System for Reproduction of In Vivo Dynamic Muscle Force.Bioengineering (Basel, Switzerland) · 2023Article
- Reliability and generalization of gait biometrics using 3D inertial sensor data and 3D optical system trajectories.Scientific reports · 2022Article
- Physical factors that differentiate body kinematics between treadmill and overground walking.Frontiers in bioengineering and biotechnology · 2022Article
- Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing.IEEE open journal of engineering in medicine and biology · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gait, the style of human walking, has been studied as a behavioral characteristic of an individual. Several studies have utilized gait to identify individuals with the aid of machine learning and computer vision techniques. However, there is a lack of studies on the nature of gait, such as the identification power or the uniqueness. This study aims to quantify the uniqueness of gait in a cohort. Three-dimensional full-body joint kinematics were obtained during normal walking trials from 488 subjects using a motion capture system. The joint angles of the gait cycle were converted into gait vectors. Four gait vectors were obtained from each subject, and all the gait vectors were pooled together. Two gait vectors were randomly selected from the pool and tested if they could be accurately classified if they were from the same person or not. The gait from the cohort was classified with an accuracy of 99.71% using the support vector machine with a radial basis function kernel as a classifier. Gait of a person is as unique as his/her facial motion and finger impedance, but not as unique as fingerprints.
Identifiers
What OpenQuestion holds
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.