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This study aims to investigate the recent literature on auditory experiences within automotive environments and discusses potential future research directions in this area. Forty-six papers obtained through the PRISMA protocol were selected from literature published over the past 15 years. The collected literature was categorized based on engine type, and a comparative analysis of research trends in the automotive industry was conducted, explicitly focusing on internal combustion vehicles (ICVs) and electric vehicles (EVs). A network analysis was performed utilizing the keywords of the papers to identify the predominant research topics. The analysis revealed research topics actively studied in existing ICV research but not covered in EV and newly emerging research topics in the EV field. The study proposes future research topics related to auditory experience design. It aims to provide insight into the design of auditory experiences in automobiles, particularly as the automotive paradigm expands to include electric and autonomous vehicles.

This paper systemically reviews and clarifies the state-of-the-art HRI evaluation studies especially for the usability of social robots. A total of 36 articles were collected through a keyword, abstract, title search from various online search engines. Afterwards, 163 measures were selected and reviewed carefully. This research was classified into two parts. In the first part, evaluation methodologies were investigated according to (1) type of stimuli on evaluation, (2) evaluation technique, and (3) criteria of participants. In the second part, assessment measures were collected and the model of attitude towards a social robot is proposed. As a result, this study suggests practical strategies for selecting appropriate methods and measures that meet specific requirements of research. The proposed hierarchical structure of assessment measures is expected to contribute to both practical use and academic use.

Disabled and elderly populations may not fully appreciate the benefits of advanced technologies and every-day products due to accessibility issues. However, the diverse characteristics of disabled and elderly users make conducting user research a challenging task for stakeholders who oversee the needs of developing accessible products. They need a tool providing rich information to empathize with the disabled and elderly users, instead of standards and documents without detailed explanation of real-world applications. Therefore, this study aims to identify accessibility issues based on the interview and observation data from 52 people with different disabilities and ages. We developed eight personas representing four different user groups under the context of home appliance usage: visually impaired, hearing impaired, spinal-cord impaired, and elderly. Each persona takes a role as a tool to understand the target users and has a persona card representing their task barriers, frustrations, needs, and quotations along with a cartoonized character. In this study, we address two common accessibility issues and two persona-specific issues within each user group. The issues are presented in stakeholder’s language personas to help them comprehend and empathize with their users.

Accidents related to electric kick scooters, which are widespread globally, are increasing rapidly. However, most of the research on them concentrates on reporting accident status and injury patterns. Therefore, while it is necessary to analyze safety issues from the user’s perspective, interviewing or conducting a survey with those involved in an accident may not return enough data due to respondents’ memory loss. Therefore, this study aims to identify the risk factors in the context-of-use for electric kick scooters based on a topic modeling method. We collected data on risk episodes involving electric kick scooters experienced by users in their daily lives and applied text mining to analyze text responses describing the risk episodes systematically. A total of 423 risk episodes are collected from 21 electric kick scooter users in South Korea over two months from an online survey. The text responses describing risk episodes were classified into nine topics based on a latent Dirichlet allocation. From the result, four risk factors can be identified by analyzing the derived topics and the cause of the risk according to the context. Moreover, we suggested design improvement directions. This study can be helpful for designing safer electric kick scooters considering safety.

Customer needs and user contexts play an important role in generating ideas for new products or new functions. This study proposes a novel framework for identifying customers’ unmet needs on online social media using the Context Tree through the Hierarchical Search of Concept Spaces (HSCS) algorithm. The Context Tree represents the hierarchical structure of nodes associated with related keywords and corresponding concept spaces. Unlike other methods, the Context Tree focuses on finding the unmet needs of customers from online social media. The proposed framework is applied to extract customer needs for home appliances. Identified customer needs are used to make user scenarios, which are used to develop new functions of home appliances.

When developing a user-oriented product, it is crucial to consider users’ affective needs. Various semantic differential (SD) methods have been used to identify affect regarding materials, and this is the most important property in products. This study aims to determine which of the three conventional SD methods (absolute evaluation 1 [AE 1], absolute evaluation 2 [AE 2], or relative evaluation [RE]) is most effective for affective evaluation. Affective evaluation was performed for vehicle instrument panels by each of these three SD methods. Two quantitative analysis methods (correlation analysis and repeated-measures ANOVA) were used to examine the performance (sample distinguishability) of each evaluation method, and it was found that both AE 2 and RE produced better results than AE 1. The correlation coefficients and p-values in correlation analysis were slightly better for RE than for AE 2. In conclusion, an affective evaluation produced better results when pairwise samples (especially one sample pair) were presented, indicating that maintaining distinct samples is very important. The clearer the difference in comparison targets is, the more accurate the evaluation results.

To develop a flexible display, smartphones with edge screens with curved displays on both sides of the main screen have been used widely. Considering that small differences in the form of smartphones, such as length, width, and thickness, could affect user experience, the effects of the curvature of the edge screen should be identified. This study aimed to investigate the effects of the curvature of edge screens on subjective feelings of smartphone users and to find out the optimal curvature. In the experiment, four nonfunctional samples with different radii of curvature (4R/6R/8R/10R) were used as samples, and participants evaluated the five subjective feelings: grip/control comfort, front/side visibility, and stability of the samples with four usage patterns. Our results revealed that 8R is the optimal curvature for the edge screen based on subjective feelings in smartphone usage. Moreover, the effect of the curvature of the edge screen on grip comfort and control comfort varied according to the usage patterns. The results of this study can be applied to design of the curvature of edge screens in smartphones and it is expected that it will contribute to improvement of users’ satisfaction.

The naval multi-function console provides various types of information to the operator. It is equipment that is key for submarine navigation, and fatal human errors can occur due to the mismatch between the console specifications and the operator’s body size. This study proposes a method for deriving console specifications suitable for the body size of Korean users. The seat height, seat width, seat depth, upper edge of backrest, and worktable height were selected as the target design variables. Using six anthropometric dimensions, a mismatch equation for each target design variable was developed. Anthropometric measures of 2027 Korean males were obtained, and the optimal specifications of the console were derived via an algorithmic approach. As a result, the match rate, considering all the target design variables, was improved from 2.57% to 76.96%. In previous studies and standards, the optimal console specifications were suggested based on the anthropometric data of a specific percentile of users, and it was impossible to quantitatively confirm the suitability of the console design for the target users. However, the method used in this study calculated the match rate using the mismatch equation devised for comfortable use of the console and a large amount of anthropometric data that represented the user population, and therefore the improvement effect of the recommended specification can be directly identified when compared to the current specifications. Moreover, the methodology and results of this study could be used for deciding the specifications of multi-function consoles in several fields, including nuclear power plants or disaster situation rooms.

A new Brain-Computer Interface (BCI) technique, which is called a hybrid BCI, has recently been proposed to address the limitations of conventional single BCI system. Although some hybrid BCI studies have shown promising results, the field of hybrid BCI is still in its infancy and there is much to be done. Especially, since the hybrid BCI systems are so complicated and complex, it is difficult to understand the constituent and role of a hybrid BCI system at a glance. Also, the complicated and complex systems make it difficult to evaluate the usability of the systems. We systematically reviewed and analyzed the current state-of-the-art hybrid BCI studies, and proposed a systematic taxonomy for classifying the types of hybrid BCIs with multiple taxonomic criteria. After reviewing 74 journal articles, hybrid BCIs could be categorized with respect to 1) the source of brain signals, 2) the characteristics of the brain signal, and 3) the characteristics of operation in each system. In addition, we exhaustively reviewed recent literature on usability of BCIs. To identify the key evaluation dimensions of usability, we focused on task and measurement characteristics of BCI usability. We classified and summarized 31 BCI usability journal articles according to task characteristics (type and description of task) and measurement characteristics (subjective and objective measures). Afterwards, we proposed usability dimensions for BCI and hybrid BCI systems according to three core-constructs: Satisfaction, effectiveness, and efficiency with recommendations for further research. This paper can help BCI researchers, even those who are new to the field, can easily understand the complex structure of the hybrid systems at a glance. Recommendations for future research can also be helpful in establishing research directions and gaining insight in how to solve ergonomics and HCI design issues surrounding BCI and hybrid BCI systems by usability evaluation.

The technical performance of recent automobiles is highly progressed and standardized across different manufacturers. This study seeks to derive a semantic space of engine acceleration sound quality for end users and identify the relation with sound characteristics. For this study, two affective attributes: ‘refined’ and ‘powerful’, and eight acoustic parameters considering revolutions per minute were used to determine the correlation coefficient for those affective attributes. In the experiment, a total of 35 automobiles were selected. Each of the 3rd gear wide open throttle sounds was recorded and evaluated by 42 adult subjects with normal hearing ability and driving license. Their subjective evaluations were analyzed using factor analysis, independent t-test, correlation analysis, and regression analysis. The prediction models for the affective dimensions show distinct differences for the revolutions per minute. From the experiment, it was confirmed that the customers’ affective response can be predicted through the acoustic parameters. In addition, it was found that the initial revolutions per minute in the accelerated condition had the greatest influence on the affective response. This study can be a useful guideline to design engine acceleration sounds that satisfy customers’ affective experience.

Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children’s sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children’s chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.