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.

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.

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.

The upper body shape is an important factor to be considered in customized suit jacket design. The present study is intended to identify the lateral upper body shapes of wheelchair users in a sagittal plane. Anthropometric data of 144 Korean male wheelchair users were collected using a tape measure and photography. Three lateral upper body shapes were identified by cluster analysis: convex back with largely protruded abdomen (31%), convex back with flat abdomen (36%), and straight back with moderately protruded abdomen (33%). Discriminant functions were constructed for the three lateral upper body shape groups, and their overall and cross-validated classification accuracies were identified as 94.4% and 89.6%, respectively. The present study found that the wheelchair users have more convex back (67%) and protruded abdomen (64%) shapes than non-wheelchair individuals. The lateral upper body shapes and discriminant functions identified in the study can be applied to a custom production process of suit jacket design for wheelchair users.

This study examines the relationship between speech characteristics and personality traits by drawing on pseudo-naturalistic conversations and on personality dimensions identified by the Myers–Briggs Type Indicator (MBTI) model which assesses four personality dimensions of introversion-extroversion, sensing-intuiting, thinking-feeling, and judging-perceiving. The speech of 30 participants was recorded and transcribed, after which a number of speech features including pitch, loudness, response time (i.e., how fast one responds to a prompt), speech rate, and discourse markers were extracted and analyzed. Results show that several speech features correspond to different personality dimensions. Specifically, speech rate as measured by words per minute reveals significant differences between judging individuals and perceiving individuals (perceiving individuals speak faster than judging individuals); there is a significant difference in response time for extroverts and introverts (extroverts respond faster); a significant difference is observed in loudness between judging and perceiving individuals (judging individuals are louder). The frequency of discourse markers is significantly higher for intuiting individuals than sensing individuals. The study discusses these findings in further inquiring the relationship between language and personality.