Publikation: The Gaitprint : Identifying Individuals by Their Running Style
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Recognizing the characteristics of a well-developed running style is a central issue in athletic sub-disciplines. The development of portable micro-electro-mechanical-system (MEMS) sensors within the last decades has made it possible to accurately quantify movements. This paper introduces an analysis method, based on limit-cycle attractors, to identify subjects by their specific running style. The movement data of 30 athletes were collected over 20 min. in three running sessions to create an individual gaitprint. A recognition algorithm was applied to identify each single individual as compared to other participants. The analyses resulted in a detection rate of 99% with a false identification probability of 0.28%, which demonstrates a very sensitive method for the recognition of athletes based solely on their running style. Further, it can be seen that these di erentiations can be described as individual modifications of a general running pattern inherent in all participants. These findings open new perspectives for the assessment of running style, motion in general, and a person’s identification, in, for example, the growing e-sports movement.
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WEICH, Christian, Manfred VIETEN, 2020. The Gaitprint : Identifying Individuals by Their Running Style. In: Sensors. MDPI. 2020, 20(14), 3810. eISSN 1424-8220. Available under: doi: 10.3390/s20143810BibTex
@article{Weich2020Gaitp-50186,
year={2020},
doi={10.3390/s20143810},
title={The Gaitprint : Identifying Individuals by Their Running Style},
number={14},
volume={20},
journal={Sensors},
author={Weich, Christian and Vieten, Manfred},
note={Article Number: 3810}
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