The Gaitprint: Identifying Individuals by Their Running Style

Sensors (Basel). 2020 Jul 8;20(14):3810. doi: 10.3390/s20143810.


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 differentiations 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.

Keywords: attractor method; human cyclic motion; individual locomotion; recognition; running quality.

MeSH terms

  • Adult
  • Athletes
  • Biometric Identification / methods*
  • Female
  • Gait Analysis*
  • Humans
  • Male
  • Middle Aged
  • Movement
  • Running*