Expertise Classification of Soccer Goalkeepers in Highly Dynamic Decision Tasks: A Deep Learning Approach for Temporal and Spatial Feature Recognition of Fixation Image Patch Sequences

Frontiers in Sports and Active Living

Enkelejda Kasneci¹  Oliver Höner²  Benedikt Hosp²  Florian Schultz² 

[1] Human-Computer Interaction, University of Tübingen, Tübingen, Germany;Institute of Sports Science, University of Tübingen, Tübingen, Germany;

关键词: eye tracking;    deep learning;    convolutional neural network;    long short-term memory;    expertise;    machine learning;   

DOI  :  10.3389/fspor.2021.692526

来源: DOAJ

【 摘 要 】

The focus of expertise research moves constantly forward and includes cognitive factors, such as visual information perception and processing. In highly dynamic tasks, such as decision making in sports, these factors become more important to build a foundation for diagnostic systems and adaptive learning environments. Although most recent research focuses on behavioral features, the underlying cognitive mechanisms have been poorly understood, mainly due to a lack of adequate methods for the analysis of complex eye tracking data that goes beyond aggregated fixations and saccades. There are no consistent statements about specific perceptual features that explain expertise. However, these mechanisms are an important part of expertise, especially in decision making in sports games, as highly trained perceptual cognitive abilities can provide athletes with some advantage. We developed a deep learning approach that independently finds latent perceptual features in fixation image patches. It then derives expertise based solely on these fixation patches, which encompass the gaze behavior of athletes in an elaborately implemented virtual reality setup. We present a CNN-BiLSTM based model for expertise assessment in goalkeeper-specific decision tasks on initiating passes in build-up situations. The empirical validation demonstrated that our model has the ability to find valuable latent features that detect the expertise level of 33 athletes (novice, advanced, and expert) with 73.11% accuracy. This model is a first step in the direction of generalizable expertise recognition based on eye movements.

【 授权许可】

Unknown