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.
Highlights
Many workers worldwide use computers, as one of the most common/necessary office tools, in performing their work tasks [1], [2] – it is estimated that more than 55% of jobs in the states involve computer use [3]
As an effort to contribute to preventing/relieving the dry eye syndrome (DES) of office workers, the current study aimed at addressing the two research questions: 1) Is it possible to develop an accurate realtime eye blink rate (EBR) monitoring system utilizing the variables for postural behaviors? and 2) If it is possible, what are the relationships between EBR and postural behaviors?
In order to address the first research question, the current study developed an accurate EBR level classification system consisting of a sensor-embedded chair and an EBR level classification algorithm
Summary
Many workers worldwide use computers, as one of the most common/necessary office tools, in performing their work tasks [1], [2] – it is estimated that more than 55% of jobs in the states involve computer use [3]. An increasing number of computer workers are performing their tasks using visual display terminals (VDTs), such as computer monitors and tablet devices. Prolonged computer work using VDTs requires continuously gazing at the screens, which could lead to dry eye syndrome (DES). The prevalence of DES has rapidly increased in recent years – tens of millions of people in the United States and up to one-third of the world’s population are suffering from DES, becoming a population health problem with significant worldwide economic costs [8]-[10]. It would be crucial to pay careful attention to the prevention of DES – prevention is cheaper, more practical and beneficial than treatment [11]
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