Development of Eye Blink Rate Level Classification System Utilizing Sitting Postural Behavior Data

Abstract

The prevalence of dry eye syndrome (DES) has rapidly increased in recent years, negatively affecting the eye health of many office workers worldwide. Although low eye blink rate (EBR) has been pointed out as one of the main risk factors for DES, it is difficult for office workers to continuously monitor and increase their own involuntary blinking, especially when they are focused on the primary work task. Thus, as an effort to help office workers correct their low EBR, the current study developed a real-time EBR level classification system utilizing sitting postural behavior data. A total of twenty participants performed typical computer tasks on a sensor-embedded chair. The participants’ eye blinking and postural behavior data were collected to develop the EBR level classification system with a random forest algorithm. After evaluating the system performance, the relationships between EBR and postural behaviors were empirically examined to help understand how the system worked for EBR level classification. As a result, the developed system showed high classification performance overall; and compared with high EBR condition, low EBR condition was related to less overall postural variability and greater extent of forward bending posture. The real-time EBR level classification system is expected to contribute to preventing/relieving DES and thereby enhancing the eye health of office workers.

Keywords

• Dry eye syndrome (DES)
• eye blink rate (EBR)
• machine learning
• postural behavior
• sensor-embedded chair