Many previous studies have identified that physiological responses of a driver are significantly associated with driving stress. However, research is limited to identifying the effects of traffic conditions (low vs. high traffic) and road types (highway vs. city) on driving stress. The objective of this study is to quantify the relationship between driving stress and traffic conditions, and driving stress and road types, respectively. In this study, electrodermal activity (EDA) signals for a male driver were collected in real road driving conditions for 60 min a day for 21 days. To classify the levels of driving stress (low vs. high), two separate models were developed by incorporating the statistical features of the EDA signals, one for traffic conditions and the other for road types. Both models were based on the application of EDA features with the logistic regression analysis. City driving turned out to be more stressful than highway driving. Traffic conditions, defined as traffic jam also significantly affected the stress level of the driver, when using the criteria of the vehicle speed of 40 km/h and standard deviation of the speed of 20 km/h. Relevance to industry: The classification results of the two models indicate that the traffic conditions and the road types are important features for driving stress and its related applications.

Objective: Artificial intelligence (AI) has been increasingly applied in various fields of science and technology. In line with the current research, medicine involves an increasing number of artificial intelligence technologies. The introduction of rapid AI can lead to positive and negative effects. This is a multilateral analytical literature review aimed at identifying the main branches and trends in the use of using artificial intelligence in medical technologies. Methods: The total number of literature sources reviewed is n = 89, and they are analyzed based on the literature reporting evidence-based guideline PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for a systematic review. Results: As a result, from the initially selected 198 references, 155 references were obtained from the databases and the remaining 43 sources were found on open internet as direct links to publications. Finally, 89 literature sources were evaluated after exclusion of unsuitable references based on the duplicated and generalized information without focusing on the users. Conclusions: This article is identifying the current state of artificial intelligence in medicine and prospects for future use. The findings of this review will be useful for healthcare and AI professionals for improving the circulation and use of medical AI from design to implementation stage.

In recent years, artificial intelligence (AI) has been applied in various fields, with rapid expansion of the scope of AI-human interactions. However, most AI technologies continue to exhibit black-box characteristics, i.e., their decisions and actions are not explainable, degrading user experience (UX). Recently, research on explainable AI (XAI), focusing on both AI performance and UX, has garnered significant attention. However, development of generalizable UX evaluation tools for AI and improvement of AI UX have not been investigated adequately. In this study, a UX evaluation tool is developed for AI based on a systematic literature review and verified using exploratory and confirmatory factor analyses. Subsequently, based on identified AI UX factors, UX of an AI defense system is upgraded. Based on user evaluation, significant improvements are confirmed in eight out of nine factors. The proposed evaluation tool is expected to serve as a cornerstone for future evaluation of AI UX advancement.

With the continuous technological enhancement of banking services, customers can avail of better, more secure services which present improved opportunities and convenience. Of the many methods available to perform banking operations, customers commonly use traditional banking, online banking, and mobile banking. Each of these existing methods has advantages and limitations that affect customer experience, trust, satisfaction, and continued intention to use such services. In this study, an attempt was made to develop and fit a model to evaluate and measure the effect of perceived characteristics on banking services. To this end, a questionnaire was administered to 91 participants in Korea to investigate their experiences in the three types of services: offline banking (traditional banking), online banking, and automated teller machines (ATM). The factor design for evaluating the user experience through the perceived characteristics of the banking system was performed by conducting exploratory and confirmatory factor analyses. The proposed model exhibited validity and reliability to evaluate the user experience in the banking system. The results obtained can help banking specialists and professionals increase the level of customers’ trust, loyalty, and intention to use their services.

The uninterrupted operation of systems with artificial intelligence (AI) ensures high productivity and accuracy of the tasks performed. The physiological state of AI operators indicates a relationship with an AI system failure event and can be measured through electrodermal activity. This study aims to model the stress levels of system operators based on system trustworthiness and physiological responses during a correct AI operation and its failure. Two groups of 18 and 19 people participated in the experiments using two different types of software with elements of AI. The first group of participants used English proofreading software, and the second group used drawing software as the AI tool. During the tasks, the electrodermal activities of the participants as a stress level indicator were measured. Based on the results obtained, the users’ stress was determined and classified using logistic regression models with an accuracy of approximately 70%. The insights obtained can serve AI product developers in increasing the level of user trust and managing the anxiety and stress levels of AI operators.

Owing to technological advancements, kiosks have become more prevalent in public places. When using such kiosks, elderly persons and people with disabilities face problems related to accessibility and usability, such as difficulties in kiosk operations such as menu selection and in accessing the kiosk space. Previous studies have usually included accessibility as a subset of usability. However, in this study, we aim to redefine the relationship between these two concepts with a focus on newly emerging kiosk devices. First, we performed a literature review to thoroughly analyze these concepts. Then, we conducted a focus group interview (FGI) targeting people with visual, hearing, and physical impairments to learn about the difficulties that these people face when using kiosks. Finally, we analyzed the characteristics of accessibility and usability related to kiosks and designed a diagram that illustrated the relationship between them. While accessibility and usability shared similarities regarding consistency and user control, they differed deeply regarding their subcategory items; many opinions on accessibility were related to essential functions, whereas many on usability were related to psychological factors such as additional functions or personal preferences. These results can be useful when creating laws and guidelines regarding the accessibility and usability of kiosks or when developing kiosk functions.