In this study, we explored the relationship between objective and subjective measures for usability evaluation in in-vehicle infotainment systems (IVISs). As a case study, four displays were evaluated based on cluster location and display orientation (that is, front–horizontal, front–vertical, right–horizontal, and right–vertical). Thirty-six participants performed tasks to manipulate the functions of the IVISs and data were collected through an electroencephalogram (EEG) sensor and questionnaire items. We analysed a model that estimated EEG-based objective indicators from subjective indicators. As a result, the objective indicators reflected the subjective indicators and were considered to explain the driver’s cognitive state. Although EEG data were collected from only four participants, this study proposed an experimental design that could be applied to the analysis of the relationship between the subject’s evaluation and EEG signals, as a preliminary study. We expect the experimental design and results of this study to be useful in analysing objective and subjective measures of usability evaluation.

Recently, the deep learning algorithm has received considerable attention and is influencing different fields including human-computer interaction (HCI). The purpose of this study is to maximize accuracy by applying deep learning to the classification of body movements. An experiment was performed to collect acceleration information on the wrist while performing seven workouts: pull up, row-barbell, bench press, dips, squat, deadlift, and military press. Participants were asked to perform each workout for ten sets repeated ten times per set. Experimental results confirm that one-dimensional convolutional neural network was the best among different algorithms including support vector machine, multi-layer perceptron, long short-term memory, and other deep convolutional neural networks. The accuracy was extremely high, 96%. The results of this experiment are applicable not only to the classification of fitness activities but also to the classification of different motions in numerous sporting events.

The importance of designated earthquake shelters has been emphasized in South Korea. However, the lack of an appropriate evaluation process for shelter candidates often leads to an ad-hoc selection of earthquake shelters, resulting in inefficient disaster management. To address this issue, this study proposes an evaluation framework based on multi-criteria decision-making to measure the suitability of earthquake shelters quantitatively. First, available indicators for shelter evaluation are extracted from the relevant literature, which are then streamlined and grouped using text mining to represent evaluation areas. Next, the selected indicators are standardized to the same value scale, and the Analytic Hierarchy Process (AHP) is used to derive the relative importance of each evaluation area to compute the weighted sum total score of each candidate. The developed evaluation framework is applied to a case study of earthquake shelter selection in the city of Songdo-dong. The results show that there are additional shelter candidates suitable for earthquake evacuation in addition to the current designated shelters. This study can serve as a basis for objective shelter selection that would improve the identification and utility of designated shelters.