ArticleComputational and structural biotechnology journal2024
Pattern analysis using lower body human walking data to identify the gaitprint.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Wobble-Board Dynamics Identify Individual Signatures of Balance Control for Clinical Assessment.Annals of biomedical engineering · 2026Article
- Recognition of Human Gait Under Asymmetric Loading.Sensors (Basel, Switzerland) · 2026Article
- Article
- NONAN GaitPrint: An IMU gait database of healthy middle-aged adults.Scientific data · 2025Article
- The individuality of plantar pressure indices enables the reduction of descriptive gait data required for diagnostic purposes.Scientific reports · 2025Article
- NONAN GaitPrint: An IMU gait database of healthy older adults.Scientific data · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
All people have a fingerprint that is unique to them and persistent throughout life. Similarly, we propose that people have a gaitprint, a persistent walking pattern that contains unique information about an individual. To provide evidence of a unique gaitprint, we aimed to identify individuals based on basic spatiotemporal variables. 81 adults were recruited to walk overground on an indoor track at their own pace for four minutes wearing inertial measurement units. A total of 18 trials per participant were completed between two days, one week apart. Four methods of pattern analysis, a) Euclidean distance, b) cosine similarity, c) random forest, and d) support vector machine, were applied to our basic spatiotemporal variables such as step and stride lengths to accurately identify people. Our best accuracy (98.63%) was achieved by random forest, followed by support vector machine (98.40%), and the top 10 most similar trials from cosine similarity (98.40%). Our results clearly demonstrate a persistent walking pattern with sufficient information about the individual to make them identifiable, suggesting the existence of a gaitprint.
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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.