Citation

Wiles TM, Kim SK, Stergiou N, Likens AD. Comput. Struct. Biotechnol. J. 2024; 24: 281-291.

Copyright

(Copyright © 2024, Research Network of Computational and Structural Biotechnology, Publisher Elsevier Publishing)

DOI

10.1016/j.csbj.2024.04.017

PMID

38644928

PMCID

PMC11033172

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.

Language: en

Keywords

Biometrics; Gait Recognition; Inertial Measurement Units; Random Forests; Support Vector Machines; Variability