Sports Science, University of Konstanz, 78464 Konstanz, Germany.
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.
识别良好发展的跑步风格的特点是运动学科中的一个核心问题。在过去几十年中，便携式微机电系统（MEMS）传感器的发展使得准确量化运动成为可能。本文介绍了一种基于极限环吸引子的分析方法，通过特定的跑步风格来识别个体。通过三次跑步过程，采集了 30 名运动员 20 分钟的运动数据，以创建个体步态特征。应用识别算法来识别每个个体与其他参与者的区别。分析结果显示，识别运动员的检测率达到 99%，误识别概率为 0.28%，这表明仅基于跑步风格识别运动员的方法非常敏感。此外，可以看出这些差异可以描述为所有参与者内在的一般跑步模式的个体修改。这些发现为跑步风格、一般运动以及例如在日益发展的电子竞技运动中的个体识别等方面的评估开辟了新的视角。
