Pattern analysis using lower body human walking data to identify the gaitprint

• Published in: Computational and structural biotechnology journal 2024-12, Vol.24, p.281-291
• Main Authors: Wiles, Tyler M., Kim, Seung Kyeom, Stergiou, Nick, Likens, Aaron D.
• Format: Article
• Language: English
• Subjects: Biometrics; Gait Recognition; Inertial Measurement Units; Random Forests; Support Vector Machines; Variability
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2024.04.017

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. [Display omitted] •People have a Gaitprint – a persistent and identifiable walking pattern.•Uniqueness of gait kinematics demonstrated 98.63% accuracy in 81 adults.•Multi-day identification is accurate at 95.47%.