The commercialization of virtual reality (VR) platforms has enabled and increased interest in the use of VR for social activities. Users are now able to enjoy a shared experience with others in an immersive social VR environment. However, current methods for designing and measuring shared user experience (UX) have been insufficient. To design and measure VR UX in a consistent manner, it is essential to develop design guidelines and evaluation tools focusing on user interfaces and interactions. In this study, a new evaluation tool that can comprehensively evaluate VR is proposed, and its validity and reliability were verified. In addition, user tasks and activities in VR were defined for a variety of users, and VR scenario guidelines were developed. Finally, the difference in UX according to device type (PC vs. HMD) was identified. This study is expected to be useful for developing social VR scenarios and evaluation tools.

Individuals with color vision deficiency (CVD) face many difficulties and limitations in their daily lives and professional activities, some of which may prove life-threatening. These negative factors necessitate the development of methods for the identification and classification of CVD. Because CVD is a vision impairment, it is crucial to determine if its presence can be predicted with eye behavior. An experiment was conducted using a driving simulator and eye-tracking glasses. The experiment included 27 people with CVD (12 with deuteranopia, 9 with deuteranomaly, 3 with protanopia, and 3 with protanomaly) and 10 people with normal color vision. Each participant performed multiple driving attempts with color-coded guidelines on the navigator to assess visual search ability. Based on data recorded by an eye tracker, the following three types of cross-validated models were developed: binary classification (presence and absence of CVD), a three-class model to recognize the absence of CVD, protanopia, and deuteranopia, and a five-class model to predict the absence of CVD, deuteranopia, deuteranomaly, protanopia, and protanomaly. These three models yielded respective accuracy levels of over 95%, 66%–78%, and 43%–52%. Overall, it was found that eye-tracking metrics have the potential to classify and predict CVD.

This study aims to understand the usability of common button colours and background choices in the existing augmented reality (AR) interface. AR is increasingly used in dynamic situations, such as driving using an AR head-up display. However, studies regarding the usability of AR interfaces, especially about the colours, are scarce. Twenty-four participants performed button selection tasks in an experiment using Microsoft HoloLens. Three measures (task completion time, the number of errors, and button colour visibility were collected for the 12 experimental conditions (3 backgrounds by 4 button colours). Significant differences were found in completion time between backgrounds and blue button visibility. On a moving background, the most effective button colour is orange, and the most minor effective button colour is blue. The results of this study can be used for colour choices in designing AR applications and devices.

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.

1. Display technologies have successfully progressed from the large cathode ray tube to small flat panel designs, such as liquid crystal display (LCD) and organic light-emitting diode (OLED), since...

ABSTRACT Augmented reality (AR) technologies are becoming increasingly popular. However, studies regarding the usability of AR interfaces are scarce. This study aims to derive the optimal usability design of the AR interface by analyzing the performance of 12 interface conditions (2 button sizes × 3 distances × 2 interaction types). An experiment was conducted using Microsoft HoloLens, in which participants performed button selection tasks with an array of 3 × 3 virtual buttons. The task completion time and the number of errors were measured along with a subjective satisfaction score. The results showed that a clicker-based interaction was more effective in task completion time, the number of errors and user satisfaction than a hand gesture. Furthermore, large buttons required a significantly shorter task completion time than small buttons. There was no significant difference in the number of errors between the two sizes. A distance of at least 80 cm from the eye to the virtual object is most favorable for a good performance. There was a significant difference in the task completion times between 40 cm and 80 cm and between 40 cm and 120 cm. These results can aid the interface designs for AR applications and devices.

Meal-assistance robots (MARs) help feed meals to users with disabilities or who need help consuming their meals. Although such MARs have been introduced in various fields, guidelines for evaluating MARs are limited. This study aims to develop comprehensive guidelines to assess the user experience (UX) of MAR designs considering its social interaction characteristics between humans and robots. Participants from three groups (patients, doctors, and caregivers) with different perspectives on MARs were recruited and a focus group interview was conducted to collect their UXs with MARs. The three groups showed different UXs with MARs in user interface design, robot arm motion, and safety and mobility. In addition, based on the literature review, eight UX features (usability, emotion, value, naturalness, assistance, acceptance, personality, and culture) are proposed to evaluate MAR interfaces. The proposed comprehensive design guideline will be particularly useful in evaluating and designing the UX of MARs.

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.

Mobile phones with a touch screen replacing traditional keypads have been introduced to the market. Few studies, however, have been conducted on the touch interface design for a mobile phone. This study investigated the effects of touch key sizes and locations on the one-handed thumb input that is popular in mobile phone interactions. Three different touch key sizes (i.e. square shape with 4mm, 7mm, and 10mm wide) and twenty five locations were examined in an experiment. The results provided two groups of touch key locations (an appropriate and an inappropriate region) with respect to three usability measures including success rate, number of errors, and pressing convenience. In addition, a hits distributions based algorithm was applied to target selection tasks, which statistically improved the performance. The results of this study could be used to design touch keys so as to enhance the usability of mobile phones with a touch screen.