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.

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.

BACKGROUND: Today, sedentary lifestyles are very common for children. Therefore, maintaining a good posture while sitting is very important to prevent musculoskeletal disorders. To maintain a good posture, the formation of good postural habit must be encouraged through posture correction. However, long-term observation is required for effective posture correction. Additionally, posture correction is more effective when it is performed in real time. OBJECTIVE: The goal of this study is to classify nine representative sitting postures of children by applying a machine learning technique using pressure distribution data according to the sitting postures. METHODS: In this study, a customized film-type pressure sensor was developed and pressure distribution data from nine sitting postures was collected from seven to twelve year-old children. A convolutional neural network (CNN) was applied to classify the sitting postures and three experiments were conducted to evaluate the performance of the model in three applicable usage scenarios: usage by familiar identifiable users, usage by familiar, but unidentifiable users, and usage by unfamiliar users. RESULTS: The results of our experiments revealed model accuracies of 99.66%, 99.40%, and 77.35%, respectively. When comparing the recall values for each posture, leaning left and leaning right postures had high recall values, but good posture, leaning forward, and crossed-legs postures had low recall values. CONCLUSION: The results of experiments indicated that CNN is an excellent classification method to classify the posture when the pressure distribution data is used as input data. This study is expected to contribute a development of system to aid in observing the natural sitting behavior of children and correcting poor posture in real time.

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.

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.