Human Eyeblink Detection in the Field Using Wearable Eye-Trackers

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.