Predicting the driver’s gaze could be important information in preventing accidents while driving. In this study, machine learning models for estimating the driver’s gaze distraction through head movement data were created and their performance was compared and evaluated. Participants wore glasses-type eye trackers and performed the task of selecting the touch screen buttons while driving. The input variable used in the model was data obtained from a 3-axis accelerometer sensor and a 3-axis gyroscope sensor, and the target variable was eye-gaze data. As a result, it was confirmed that the gaze area could be estimated with a precision, sensitivity, specificity, and F1-score of 72.1%, 72.5%, 66.0%, and 69.3%, respectively, only with the head movement sensing data. The model trained using time-series datasets had higher performance than using non-time series datasets. This study presented one alternative that could be used to determine the driver’s status with an inexpensive sensor.

We analyzed the button input method of infotainment systems as part of the in-vehicle information system (IVIS) interface and related research. As IVIS becomes more complex and the need for driver-vehicle interaction increases, so does the potential risk that accompanies driver input. In this study, we propose to analyze the influence of the touch button shape, background, button and text color combination, and the presence/absence of a button border on the usability of the button input operation, which has not been studied before. Thus, participants were made to perform a button press operation in various combined conditions in a driving simulator. For keypads of the same diagonal length, we recommend a square-shaped touch keypad. In addition, the use of an edge and a specific color combination has a positive effect on the overall task performance and user satisfaction. These results are expected to be useful in the development of effective IVIS information display and input.

Virtual reality (VR) devices have recently become popular; however, research on input methods for VR devices is lacking. The main input methods of current commercial devices can be classified into two categories: manual selection using a controller and gaze selection. This study aims to derive the optimal input method and timing for VR devices by analyzing the performance of these input methods. A study is conducted in which participants wear a VR headset and select an activated input button from a 3 × 3 array on a VR device with two button sizes and two input methods. The manual selection method exhibits a shorter task completion time but a greater error number compared to the gaze selection method. For the gaze selection method, the task completion time and target search time were shortest when the gaze timing was 1 s. Further, the button size was determined to be statistically significant only when the manual selection method was used. The results of this study can be used as a reference in future VR user experience and product design.

Amid the increasing popularity of smartphones fitted with touch screens, many studies have been conducted on touch screen input methods. In contrast to standardized stylus pens, the size and anthropometric features of the fingers can be vastly different depending on the person. These features significantly impact the touch screen’s usability for an individual. In this paper, the relationship between the anthropometrical dimensions of the thumb (i.e., length, breadth) and touch task performance was analyzed. Based on the results, subjects with relatively longer fingers required more time on average to complete the task. However, no correlation was evident between the thumb length and number of errors, or among the thumb breadth, task completion time, and error frequency. The results of this study are expected to be useful in the development of a mobile interface that considers the specific features of the user’s thumb.