Wiles Tyler M, Kim Seung Kyeom, Stergiou Nick, Likens Aaron D
Department of Biomechanics at the University of Nebraska at Omaha, 6160 University Dr S, Omaha, NE 68182, USA.
Department of Physical Education and Sport Science, Aristotle University, Thermi, AUTH DPESS, Thessaloniki 57001, Greece.
Comput Struct Biotechnol J. 2024 Apr 12;24:281-291. doi: 10.1016/j.csbj.2024.04.017. eCollection 2024 Dec.
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.
所有人都有独一无二且终生不变的指纹。同样，我们提出人也有步纹，即一种包含个体独特信息的持续行走模式。为了提供独特步纹的证据，我们旨在基于基本时空变量识别个体。招募了81名成年人，让他们佩戴惯性测量装置，在室内跑道上以自己的节奏行走4分钟。每位参与者在相隔一周的两天内共完成18次试验。将四种模式分析方法，即a)欧几里得距离、b)余弦相似度、c)随机森林和d)支持向量机，应用于诸如步长和步幅等基本时空变量，以准确识别个体。随机森林的准确率最高（98.63%），其次是支持向量机（98.40%），余弦相似度中最相似的前10次试验的准确率为98.40%。我们的结果清楚地表明存在一种持续的行走模式，其中包含足以识别个体的信息，这表明步纹的存在。
