Abstract
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.
Highlights
Describing and understanding the characteristics of a well-developed and efficient running style is a central issue in athletic sub-disciplines
The results indicate that the chance of having a false positive allocation is, on average, as small as 0.28%
Based on the outcome of [22] and the analysis of the data that were collected for the current study, it was shown (Figure 6), that, a person’s motion was accompanied by a transient effect at the onset, it subsided by 10 min. at the latest
Summary
Describing and understanding the characteristics of a well-developed and efficient running style is a central issue in athletic sub-disciplines. The identification of subject-specific running characteristics is crucial for approximating towards a better understanding of running efficiency from a biomechanical standpoint. Experimental psychology has demonstrated high identification rates of so-called bio-motion animations [3]. While subjective observation data [4,5], the understanding and interpretation of human motion recognition, is certainly not a new endeavor, smart surveillance, robotics, medical applications and others, as well sports and exercise have taken advantage of the growing technology and methods ([6], Table 1) within the last two decades. Within the last decade, with the field of e-sports another interesting area of application has emerged. Since 2006, when Nintendo released its Wii console, motion-controlled gaming systems have evolved so much that they are used for therapeutic [7]
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