Boccia is a para-sport that can be played solely by throwing the ball, and there are commercially available high-functionality, high-cost boccia systems equipped with various assistive features. In contrast, the authors have developed a simple and low-cost boccia system and demonstrated its effectiveness in practical use. In this study, we designed a basic boccia system that allows for customization to ac-commodate diverse individual needs. Specifically, we first developed an easy-to-make and user-friendly basic system equipped with minimal essential functions. Next, we evaluated the system ’s operation at a special-needs school and an after-school day service to identify necessary assistance. Additionally, we held work-shops focusing on digital transformation (DX) in manufacturing to verify the fea-sibility of customization by general users. Based on these findings, we assessed the potential for assisting people with a variety of disabilities. As our next step, we aim to make these developments open source to enable broader implementation.

The global prevalence of myopia continues to rise. While objective refraction methods such as autorefractors are available, their high cost and lack of portability limit their application in settings such as developing countries and school-based screenings. In this study, we developed a novel method for refractive error measurement using a smartphone camera, based on the principle of photorefraction. The proposed method was validated through optical simulations and model eye experiments. Moderate correlations were observed between simulation results and model eye data for the width of retroillumination images (r = 0.637, p < .001). Furthermore, analysis of retroillumination images from human eyes enabled estimation of refractive power, which showed a strong correlation with measurements obtained from a conventional autorefractor (r = 0.900, p < .001). These findings suggest that our smartphone-based approach may serve as a low-cost and portable solution for refractive error screening, particularly in resource-limited environments.

This study investigates the effectiveness of binaural audio, representing meanings of words, in English vocabulary learning. The Bilingual Dual Coding Theory is utilized as a theoretical framework, with emphasis placed on the interplay between verbal and sensory systems for optimal learning. An expressive sound archive of 40 English words was developed using binaural recording technology, with the intention of providing auditory representations of word meanings. A within-participant experiment was conducted with 20 high school students, who memorized two sets of words under both binaural and monaural audio conditions. Immediate and one-week post-tests using multiple-choice questions revealed significantly higher retention rates when students learned with binaural audio. A subsequent two-way repeated measures ANOVA indicated a main effect for audio condition and time progression, but no significant interaction, suggesting that binaural audio consistently enhances vocabulary retention. Observations of participants’ gestures and vocal repetitions supported the idea that interactive, multidimensional sensory input can reinforce memory formation. A follow-up EEG study exploring neural correlates found a non-significant trend linking theta wave ERSP with correct responses, aligning with previous research on theta oscillations and memory. The findings under discussion highlight the potential of binaural audio as a powerful tool in computer-assisted language learning, paving the way for further exploration of its long-term benefits and the mechanisms of neural activity involved in vocabulary acquisition.

This study proposes a method using generative AI to support associative memory formation between memory targets and location cues in the Method of Loci (MoL), which is often challenging for beginners. Based on the levels-of-processing model from cognitive psychology, our system generates three types of associative examples (physical, phonological, and semantic), allowing learners to select associations that best match their cognitive style. Experimental results with 30 university students showed that 80% of participants found it easier to create associations with AI examples, and memory retention tests after 24 hours demonstrated significantly improved recall rates. Participants particularly valued the “impressive and novel perspectives” provided by AI examples. However, when AI examples significantly differed from learners’ thought patterns, they could potentially hinder memory formation. Future work should focus on analyzing learners’ cognitive patterns beforehand and incorporating this information into AI prompts to create more personalized associative examples. This research provides guidelines for utilizing generative AI in education while suggesting improvement.

Online conversations through platforms like Zoom or AR-enabled dialogues with smart glasses offer the unique advantage of overlaying relevant information on screens. Recent research has introduced systems that augment speakers with speech bubbles containing either identical or alternative text to their verbal statements, aiming to enhance dialogue. However, these systems present an interpretive challenge: when spoken content and text in speech bubble convey different attitudes, it remains unclear which source listeners prioritize to form impressions of speakers. This study investigates how speech bubble augmentation influences listeners’ interpretation of speaker attitudes. We categorized these influences into three types: clarification, amplification/attenuation, and transformation of attitudes. Through three online experiments designed to examine each category, we confirmed all three types of influence. Notably, listeners demonstrated a tendency to prioritize speech bubble content over verbal statements when interpreting speaker attitudes. Our findings provide fundamental insights into the effects of speech bubble augmentation on human communication in digital interaction environments while acknowledging limitations in experimental scope and demographics that necessitate further research across diverse contexts.

Listening to audio content, such as podcasts and audiobooks, is one of the ways people engage with knowledge. Listening affords people more mobility than reading by seeing, thus broadening learning opportunities. This study explores the potential applications of large language models (LLMs) to adapt text documents into audio content, addressing the lack of listening-friendly materials for niche content like research papers. To explore this potential, we developed PaperWave (https://paperwave.app), a prototype that transforms academic paper PDFs into conversational podcasts. Our autobiographical design with a field study involving 11 participants (including the authors) documents the nuanced understanding of the design process and the shifts in engagements with knowledge. The findings highlight the importance of considering listeners’ interaction with their environment when designing document-to-audio systems.

Foot-based input is a promising method for enabling hands-free interaction in VR environments, where traditional hand-based input may be limited or occupied. This study explores the interaction characteristics of foot gestures in two fundamental 3D tasks: selection and drag-and-drop. We designed four foot gesture techniques and evaluated their performance in a virtual environment displayed through a head-mounted display (HMD). In the first experiment, we measured target selection performance using Fitts’ law. In the second, we assessed drag-and-drop performance. The experimental results highlight performance trade-offs among the four techniques, depending on the interaction task. Based on these observations, we discuss considerations for selecting appropriate foot gestures in VR interfaces. Keywords €b0: HMD, Foot Gesture, User Interface, 3D Pointing, Object Manipulation

Recently, remote control robots have been increasingly utilized in various situations, such as disaster sites and hazardous areas. Low-cost game controllers are widely used as general remote control devices. However, in narrow environments such as tight passages, operation tends to be time-consuming and mentally taxing. To address this problem, we focused on overhead maps that can be generated from the robot’s sensor. We developed a method that enables the robot to move autonomously, by allowing users to draw paths on the overhead map. To evaluate this method, we conducted experiments with 10 participants aged 20 to 40. The evaluation consisted of two types of experiments: the “operation accuracy evaluation, ” which measures the precision of control, and the “ traversal experiment, ” which evaluates traversal time and usability. As a result, the minimum passable width when navigating between obstacles improved by 11.3%. Additionally, in the traversal experiment, the average traversal time was reduced by 7.6%, and the average SUS score which measures usability, increased by 19.5.

Motor learning, understood as the embodied wisdom learning, is a tough, highly individual-specific process in which individuals actively generate their own questions through their body in the course of living. This study hypothesizes that learners can generate such questions by keenly attuning to the “ Hyojo ” of the body in motion―the moving body ’s vivid, immediate gestalt―and designs a web application, HJ-Playground (HJP), to scaffold this attunement. In HJP, moving point clouds are rendered from body-motion data, and users are encouraged to draw auxiliary lines in various styles across the points to compose abstract figures (“ Hyojo figures ”), to practice “ embodied meta-cognition”―a cognitive methodology that actively articulates and documents“questions, ” from tacit to explicit forms―about what they apprehend there, and to express the Hyojo in onomatopoeia as well. I conducted learning practices with a street dancer and a track-and-field triple-jump athlete, each using their own motion data. By making inventive use of HJP, participants oriented to relations among body parts, noticed subtle somatic sensations, and re-cast Hyojo figures as scenes in everyday domains distinct from the original movement, thereby weaving questions autonomously. Engagement with HJP also influenced their day-to-day motor-learning activities. HJP encourages a fundamental inquiry into the relationship between one ’s lived words and body. This suggests a form of support for learning embodied wisdom that does not simply make learning easier or faster, but instead deepens meaning-making.

The amount and rate of skin temperature change are important factors in the generation of thermal sensation, but there is a few research on quantifying the stimulation patterns and starting time of thermal sensation. In the presentation of thermal sensation, there is a delay in the starting time between presentation and thermal sensation. To improve the sense of presence, it is important to control the starting time of the sensation. In this study, we focus on the relationship between the starting time of thermal sensation and the time-integral amount of skin temperature change. We proposed the stimulus pattern determination and thermal presentation methods that control the skin temperature change to perceive the desired thermal sensation at the desired starting time and sensory intensity. From the experimental results, the accuracy of the starting time ranged from 0.59 to 1.54 s.

Preventing traffic accidents of elderly drivers is one of the important social issues. In this study, in order to gain insight about the relationship between elderly drivers' awareness of driving and their driving behavior, we focused on “compensatory driving”, which is driving behavior that is performed to compensate for the decline in physical and mental functions due to aging, and conducted a survey on driving awareness and collected driving behavior data using a drive recorder. Driving behavior was evaluated using a set of criteria, and the relationship with the results of the awareness survey questionnaire was statistically analyzed using nonparametric methods. As a result, some of the compensatory driving, although intended to ensure safe driving, was found to be associated with undesirable driving behaviors depending on the driving situation. These findings suggest that compensatory driving does not always contribute to safety, and that drivers should remain aware of its potential limitations.

The purpose of this study is to experimentally evaluate the impact of the “familiarity” of a VR disaster experience environment on the experiencer’s awareness of disaster prevention. For this purpose, an evaluation experiment was conducted. In the experiment, participants were asked to take pictures of the environment in which they normally live, and to experience virtual earthquake and fire in the familiar environment created from the pictures and in the non-familiar one created in advance. After experiencing each disaster experience environment, they were asked to answer a questionnaire about their awareness of disaster prevention, which included a sense of reality, a sense of fear, a sense of familiar environment, recommendation intention, disaster risk perception, anxiety and disaster prevention behavioural intention to compare the effects of each disaster experience environment on the awareness of disaster prevention. The results show that experiencing VR disaster in a familiar environment may increase their sense of familiar environment and sense of fear regardless of the type of disaster. However, even when the environment is unfamiliar, if it contains objects identical to those found in familiar settings and those objects could potentially harm participants, the experience was also found to have the potential to increase their awareness of disaster prevention.

Self-ordering systems using touch screens have become increasingly common in restaurants and retail stores, but they primarily rely on visual information, making them difficult for visually impaired individuals to use. Previous research has attempted to address this issue by developing support systems based on databases of touch screens, allowing structured information to be provided in an accessible format. However, maintaining such databases is time-consuming, labor-intensive, and impractical for all touch screens, making it difficult to support new or unregistered systems. To overcome this limitation, this study proposes a system that extracts button information from touch screens images using a Vision Language Model, structures the extracted data, and presents it to visually impaired users. The system is evaluated through an experiment in which a visually impaired participant uses the system to order sushi via a touch screens. The study assesses the system’s usability and effectiveness, identifies potential challenges, and discusses possible improvements for future development. This research aims to enhance accessibility and usability for visually impaired individuals in self-ordering environments.