ArticleSensors (Basel, Switzerland)2020
The Gaitprint: Identifying Individuals by Their Running Style.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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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
10 citing papers in PubMed.
- The Heel-Strike Fallacy and Cadence Beliefs in Distance Running: A Narrative Review.Sports medicine - open · 2026Review
- How should we name the way people run? Advocating the use of 'running repertoire'.BMJ open sport & exercise medicine · 2026Article
- Pattern analysis using lower body human walking data to identify the gaitprint.Computational and structural biotechnology journal · 2024Article
- NONAN GaitPrint: An IMU gait database of healthy young adults.Scientific data · 2023Article
- Systematic reduction of leg muscle activity throughout a standard assessment of running footwear.Journal of sport and health science · 2022Article
- Discovering the sluggishness of triathlon running - using the attractor method to quantify the impact of the bike-run transition.Frontiers in sports and active living · 2022Article
- Prominent Fatigue but No Motor Fatigability in Non-Hospitalized Patients With Post-COVID-Syndrome.Frontiers in neurology · 2022Article
- Isolating the Unique and Generic Movement Characteristics of Highly Trained Runners.Sensors (Basel, Switzerland) · 2021Article
- Individuality decoded by running patterns: Movement characteristics that determine the uniqueness of human running.PloS one · 2021Article
- Transient Effect at the Onset of Human Running.Biosensors · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recognizing the characteristics of a well-developed running style is a central issue in athletic sub-disciplines. The development of portable micro-electro-mechanical-system (MEMS) sensors within the last decades has made it possible to accurately quantify movements. This paper introduces an analysis method, based on limit-cycle attractors, to identify subjects by their specific running style. The movement data of 30 athletes were collected over 20 min. in three running sessions to create an individual gaitprint. A recognition algorithm was applied to identify each single individual as compared to other participants. The analyses resulted in a detection rate of 99% with a false identification probability of 0.28%, which demonstrates a very sensitive method for the recognition of athletes based solely on their running style. Further, it can be seen that these differentiations can be described as individual modifications of a general running pattern inherent in all participants. These findings open new perspectives for the assessment of running style, motion in general, and a person's identification, in, for example, the growing e-sports movement.
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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.