Taku Harada

Evaluation Model of Cognitive Distraction State Based on Eye Tracking Data Using Neural Networks

Eye tracking reveals a persons state of mind. Thus, representing personal cognitive states using eye tracking leads to objective evaluations of these states, and this can be applied to various fields. In this paper, we propose a model that evaluates the degree of personal distraction based on eye tracking. Moreover, we apply the proposed model to eye tracking for a person driving a car.

Evaluation model of cognitive distraction state based on eye-tracking data using neural networks

Eye tracking reveals a persons state of mind. Thus, representing personal cognitive states using eye tracking leads to objective evaluations of these states, and this can be applied to various fields. In this paper, we propose a model that evaluates the degree of personal distraction based on eye tracking. Moreover, we apply the proposed model to eye tracking for a person driving a car.

Designing a Car-Driver's Cognitive Process Model for considering Degree of Distraction

A distracted state of a driver affects car driving state. The eye tracking can reveal an individuals psychological state. In this paper, we design a drivers cognitive process model by clearly indicating the relations between cognitive states, such as perception and memory, in the process to produce the driving action using the eye tracking data. It is important to consider degree of distraction. Therefore, we consider a cognitive distraction expressed both serially and quantitatively in the model. In this modeling, we utilize a production system framework, and the cognitive distracted state is managed by a module in the production system.

SS3: A design of the cognitive process model for a car driver considering quantitatively expressed distraction

Eye tracking can reveal an individuals state of mind. In this paper, taking into consideration that cognitive distraction that may be expressed both serially and quantitatively, we designed the Drivers Cognitive Process Model by expressing the relationships between cognitive states, such as perception and memory, from eye tracking which we utilized as an input to the driving action which we considered as an output. It is expected that driving state can be expressed accurately by analyzing distraction.

Detecting Cognitive Distraction using Random Forest by Considering Eye Movement Type

Detecting distracted states can be applied to various problems such as danger prevention when driving a car. A cognitive distracted state is one example of a distracted state. It is known that eye movements express cognitive distraction. Eye movements can be classified into several types. In this paper, the authors detect a cognitive distraction using classified eye movement types when applying the Random Forest machine learning algorithm, which uses decision trees. They show the effectiveness of considering eye movement types for detecting cognitive distraction when applying Random Forest. The authors use visual experiments with still images for the detection.

Optimal path planning considering robustness based on the form of the travel time function

In this paper, we propose an optimization model for planning a robust path against changes in traffic volume. Robustness is based on the form of the travel time function. The proposed model can be applied not only when traffic volume increases but also when it decreases. In addition, the proposed model can set the ratio of consideration by a parameter depending on whether the traffic volume is increasing or decreasing. The effectiveness of the proposed model is evaluated using actual traffic data from the Kanto region in Japan.

Considering eye movement type when applying random forest to detect cognitive distraction

Eye movements are well known to express cognitive distraction. Detecting cognitive distraction can help to prevent work-related accidents; thus, it is very useful to detect cognitive distraction using eye movements. Eye movements can be classified into various types. In this paper, we apply an identification-based machine learning algorithm considering eye movement types. We apply Random Forest as the machine learning algorithm. We show the effectiveness of considering eye movement types when applying Random Forest to detect cognitive distraction.

The Probability of the Occurrence of Negative Estimates in the Variance Components Estimation by Nested Precision Experiments

Nested experiments are commonly used to estimate variance components especially for the precision experiments. The ANOVA (analysis of variance) estimators are expressed as linear combinations of the mean squares from the ANOVA. Negative estimates can occur, as the linear combinations are usually including negative coefficients. The probability of the occurrence of negative estimates depends on the degrees of freedom of the mean squares and the true values of the variance components themselves. Based on the probability, some practical recommendations concerning the number of laboratories can be derived for the precision experiments.

Choice of Control Interval for Controlling Assembly Processes

Many industrial products are produced by continuous processes. Time series analysis and control theory have been widely applied to such processes. There are also many industrial products that are produced by assembly processes. In those processes, time series analysis and control theory are usually not required. However, there exist assembly processes that do need time series analysis for effective process control. Problems are discussed when time-series analysis methodology is applied to specific assembly processes for effective process control, especially if the number of products is high. Influential factors like the control interval and dead time of the process under consideration are considered.