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Pattern Analysis Using Lower Body Human Walking Data to Identify the Gaitprint
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  • Tyler Wiles ,
  • Seung Kyeom Kim ,
  • Nick Stergiou ,
  • Aaron Likens
Tyler Wiles
University of Nebraska at Omaha

Corresponding Author:[email protected]

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Seung Kyeom Kim
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Nick Stergiou
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Aaron Likens
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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. Healthy young 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 (99.38%) was achieved by the support vector machine and by the top 5 and top 10 most similar trials from cosine similarity. Our results clearly demonstrate a persistent walking pattern with sufficient information about the individual to make them identifiable, suggesting the existence of a gaitprint.