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.

中文翻译：

---

步态图：通过跑步方式识别个体。

认识到发达的跑步风格的特征是运动子学科的核心问题。在过去的几十年中，便携式微机电系统（MEMS）传感器的发展使得精确地量化运动成为可能。本文介绍了一种基于极限环吸引子的分析方法，可以根据受试者的特定跑步方式对其进行识别。在20分钟内收集了30名运动员的运动数据。在三个连续的会话中创建个人步态。与其他参与者相比，应用了识别算法来识别每个个体。分析得出检出率为99％，错误识别率为0.28％，这证明了仅基于运动员的跑步方式来识别运动员的非常灵敏的方法。进一步，可以看出，这些差异可以描述为所有参与者固有的一般跑步模式的单独修改。这些发现为评估跑步风格，总体运动状况以及在不断发展的电子竞技运动中对人的识别提供了新的视角。