Eyeblink dynamics, including eyeblink rate, duration, and timings, are widely recognized to reflect internal states such as cognitive and psychological states. Gaze movement strategies have been widely investigated beyond laboratory settings to infer the internal states; however, eye blink behavior has not been well investigated. Consequently, the relationship between eyeblink dynamics and internal states under in-the-wild behavior is still unclear. Eyeblink detection algorithms for head-worn eye trackers are susceptible to extreme eye angles and illumination changes; this limits the study of eyeblink in natural behavior. This issue is addressed by a supervised-machine-learning-aided data analysis pipeline that offers a reliable baseline for eyeblink detection, even when using datasets acquired under harsh conditions. Our pipeline consists of training a deep convolutional neural network model by a transfer learning approach, prediction using the model, and efficient performance monitoring incorporating eyeblink characteristics. In the case study, we analyze eye videos during formula car driving data by our proposed method and the default method of an off-the-shelf eyetracker. Our method predicts better than the present software in terms of the proportion of the predicted eyeblinks with the appropriate duration range as spontaneous eyeblinks and estimated precision. The results suggest that our approach could serve as a valuable tool for cognitive, psychological, and human factors research by enabling high-quality eyeblink dynamics data acquisition from various eyetracking data.

The neural efficiency hypothesis predicts the contribution of neural synchronization to sports performance. Professionally-trained athletes exhibit changes in low-frequency neural synchronization in task-relevant cortical regions during the processing of tasks related to the competition. Of note, the increase in alpha/beta oscillations reduces the allocation of brain resources to task-irrelated processing, resulting in enhanced efficiency. Previous studies have only dealt with single-action sports competitions as experimental tasks. The optimization of single-action processing can also improve outcomes in match format games, which are more complex and prolonged. To test the contribution of low-frequency oscillations of task-relevant cortical regions towards the outcome of match format gaming, this study focused on esports athletes engaging in a fighting video game (FVG). We examined the association between EEG results during round-intervals of FVGs and the consequences of the rounds. The beta power of the parietal area increased just before a winning round. The findings suggest that the neural efficiency hypothesis can be extended beyond the performance of single actions to affect the outcomes of a match.

This paper aims to establish a novel VR system for evaluating the performance of baseball batters. Existing VR systems for sports have been utilized as a tool for image training. In order to move such VR systems to the next stage, we introduce functions that sense the users' reaction to the VR stimulus. Our VR system has three features; (a) it synthesizes highly realistic VR video from the data captured in actual games, (b) it estimates the reaction of the user to the VR stimulus by capturing the 3D positions of full body parts, and (c) it consists of off-the-shelf devices and is easy to use. Our demonstration provides users with a chance to experience our VR system and give them some quick feedback by visualizing the estimated 3D positions of their body parts.

This study aims at ascertaining the applicability of a virtual reality environment (VRE) to sports training. Hitting an incoming object is one of the most common actions in various ball games, in which players are required to move to a suitable position and hit the object in a split second; this is a complicated task requiring spatio-temporal reaction to the object. Due to this complexity, how a VRE can serve as a training environment still remains an open question. In the work reported in this paper, we investigated the idea of substituting a VRE for an actual environment for training on the task of hitting a baseball. By focusing on the batter's temporal behavior with real and virtual environments, we clarified factors that contribute to the batter's reaction. This helped us understand how training VREs can be effectively utilized and the VRE requirements needed for sports training.