Development of Estimating Equation of Machine Operational Skill by Utilizing Eye Movement Measurement and Analysis of Stress and Fatigue

Abstract

For an establishment of a skill evaluation method for human support systems, development of an estimating equation of the machine operational skill is presented. Factors of the eye movement such as frequency, velocity, and moving distance of saccade were computed using the developed eye gaze measurement system, and the eye movement features were determined from these factors. The estimating equation was derived through an outlier test (to eliminate nonstandard data) and a principal component analysis (to find dominant components). Using a cooperative carrying task (cc-task) simulator, the eye movement and operational data of the machine operators were recorded, and effectiveness of the derived estimating equation was investigated. As a result, it was confirmed that the estimating equation was effective strongly against actual simple skill levels ( = 0.56-0.84). In addition, effects of internal condition such as fatigue and stress on the estimating equation were analyzed. Using heart rate (HR) and coefficient of variation of R-R interval ( vrri ). Correlation analysis between these biosignal indexes and the estimating equation of operational skill found that the equation reflected effects of stress and fatigue, although the equation could estimate the skill level adequately.

Classification of Expert-Novice Level Using Eye Tracking And Motion Data via Conditional Multimodal Variational Autoencoder

“…With the recent rapid spread of wearable devices, many researchers have analyzed expert-novice differences by using sensor data collected from wearable devices [5][6][7][8]. They used eye tracking data during machine operation [5] and flight operation [7], electroencephalogram (EEG) signals during programming language comprehension [6], and near-infrared spectroscopy (NIRS) signals during the teaching of students [8]. Furthermore, previous studies [9,10] applied machine learning approaches to classify the expert-novice level.…”

“…However, persons generally take various actions, and the differences in sensor patterns depending on actions become more remarkable than the differences between experts and novices. Furthermore, since previous expert-novice analyses [5][6][7][8][9][10] used sensor data acquired from a single wearable device, it is difficult to analyze expert-novice differences from various aspects.…”