ArticlePloS one2021
Individuality decoded by running patterns: Movement characteristics that determine the uniqueness of human running.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Mind to Move: A Narrative Review of Individualized Running Biomechanics Beyond the Spring-Mass Model.Sports medicine (Auckland, N.Z.) · 2026Review
- Gait signature changes with walking speed are similar among able-bodied young adults despite persistent individual-specific differences.Scientific reports · 2024Article
- Gait signature changes with walking speed are similar among able-bodied young adults despite persistent individual-specific differences.bioRxiv : the preprint server for biology · 2024Article
- Regional differences in the biological variability of plantar pressure as a basis for refining diagnostic gait analysis.Scientific reports · 2024Article
- Subject-specific sensitivity of several biomechanical features to fatigue during an exhaustive treadmill run.Scientific reports · 2024Article
- Unveiling individuality in the early phase of motor learning: a machine learning approach for analysing weightlifting technique in novices.Frontiers in bioengineering and biotechnology · 2024Article
- Neural network and layer-wise relevance propagation reveal how ice hockey protective equipment restricts players' motion.PloS one · 2024Article
- Identification of subject-specific responses to footwear during running.Scientific reports · 2023Article
- Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle-consistent generative adversarial networks.Frontiers in bioengineering and biotechnology · 2023Article
- Modeling biological individuality using machine learning: A study on human gait.Computational and structural biotechnology journal · 2023Article
- Estimating Running Ground Reaction Forces from Plantar Pressure during Graded Running.Sensors (Basel, Switzerland) · 2022Article
- Explaining the differences of gait patterns between high and low-mileage runners with machine learning.Scientific reports · 2022Article
- New Insights for the Design of Bionic Robots: Adaptive Motion Adjustment Strategies During Feline Landings.Frontiers in veterinary science · 2022Article
- Isolating the Unique and Generic Movement Characteristics of Highly Trained Runners.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human gait is as unique to an individual as is their fingerprint. It remains unknown, however, what gait characteristics differentiate well between individuals that could define the uniqueness of human gait. The purpose of this work was to determine the gait characteristics that were most relevant for a neural network to identify individuals based on their running patterns. An artificial neural network was trained to recognize kinetic and kinematic movement trajectories of overground running from 50 healthy novice runners (males and females). Using layer-wise relevance propagation, the contribution of each variable to the classification result of the neural network was determined. It was found that gait characteristics of the coronal and transverse plane as well as medio-lateral ground reaction forces provided more information for subject identification than gait characteristics of the sagittal plane and ground reaction forces in vertical or anterior-posterior direction. Additionally, gait characteristics during the early stance were more relevant for gait recognition than those of the mid and late stance phase. It was concluded that the uniqueness of human gait is predominantly encoded in movements of the coronal and transverse plane during early stance.
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