Article Biology Life Sciences and Basic Medicine

Pilot study on changes in frontal oxygenated hemoglobin levels in beginners during practice of Karate Forms

Karate exhibits characteristics of several movement forms. This study investigated and compared cerebral oxygenated hemoglobin (∆oxyHb) levels, an indicator of brain activity, during form performances in Karate beginners. Significant increases in ∆oxyHb levels in the frontal region of the brain were observed during the performance of Karate Forms A, B, and C, as well as a radio exercise. Increases during Forms A and B were significantly greater than those during Form C and radio exercise. Rated perceived exertion (RPE) increased from Karate Forms A to C, with radio exercise exhibiting the lowest RPE. Although previous studies have suggested that cerebral ∆oxyHb tends to increase with rated perceived exertion, the relatively smaller ∆oxyHb change observed during Form C might be interpreted as reflecting greater cognitive effort and motor control demands in beginners. The modest ∆oxyHb response during the radio exercise might be related to higher motor familiarity and reduced cognitive requirements. Overall, these observations may imply that Karate practice is capable of engaging frontal brain regions in beginners, and that the degree of activation might be influenced by cognitive effort, motor control load, and familiarity with the movements.

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Article Agriculture Food Sciences Information Sciences Psychology Psychology and Education

Why do you recall that smelly food? Effects of childhood residence region and potential reinforcing effect of marriage

Little is known about why smelly foods have been maintained for long periods of time despite their unpleasant smell. Previous research suggests that regional background and family environment during upbringing influence food selection. Based on those findings, we hypothesized that living in a region where traditional smelly foods are produced and consumed during one's childhood would enhance the recall of such foods in adulthood. Additionally, we proposed that this childhood experience would positively influence the effect of marriage on an individual’s recall of smelly foods. We selected *kusaya* as the chosen smelly food and examined how, as the main effect, living in the Kanto region of Japan until the age of 20 impacts an individual’s *kusaya* recall. Furthermore, we explored the moderating effect of this upbringing on *kusaya* recall and marital status by sequentially inputting variables into a logistic regression model. Both effects were confirmed. This study contributes to the understanding of how characteristic smelly foods can be preserved by clarifying the factors that enhance their recall, using *kusaya* as an example.

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Article Education Psychology and Education

Exploring Undergraduate Students’ Transformative Learning Experiences through an eHealth Literacy Workshop in Japan

The purpose of this study was to explore Japanese undergraduate students’ transformative learning experiences during an eHealth literacy workshop. Grounded in transformative learning theory, the study employed a descriptive qualitative approach using an explanatory case study design. Six undergraduate students who participated in the workshop completed a demographic questionnaire and a semi-structured interview. Data were analyzed using the constant comparative method, resulting in three major themes: (Theme I) learning about searching strategies to identify problematic assumptions, (Theme II) task-oriented learning to develop evaluation skills, and (Theme III) necessity of learning eHealth literacy for students who live alone. The findings emphasize the importance of prioritizing eHealth literacy in Japanese universities to prevent students from engaging in risky health information practices. Therefore, we hope the findings will contribute to the development of both formal and informal eHealth literacy education in Japanese universities, enhancing students’ capacity to become effective and responsible seekers of online health information.

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Article Architecture Biology Engineering in General General Medicine Life Sciences and Basic Medicine Social Medicine

Accuracy of peripheral oxygen saturation (SpO₂) at rest determined by a smart ring: A Study in Controlled Hypoxic Environments

Peripheral oxygen saturation (SpO₂) measured by small wearable devices has garnered significant attention as a tool for detecting vital signs in acute and chronic diseases. However, the accuracy of such devices, particularly smart rings measuring SpO₂ at the finger base, remains underexplored. This study aimed to validate the accuracy of SpO₂ measurements obtained from a smart ring by comparing them with a clinical pulse oximeter in a controlled hypoxic environment. A total of 10 active males and females lay in the supine position at rest in a hypoxic environment, where oxygen saturation was maintained between 80% and 100% (normoxic levels). The participants wore a smart ring with photoplethysmography at the base of the second and third fingers of the dominant hand and a clinical pulse oximeter on their fingertips. To validate the accuracy of SpO₂ measured by a smart ring, leave-one-out cross-validation was performed, comparing root-mean-square error (RMSE) for 6793 data samples. The mean SpO₂ was 88.3 ± 7.2% for the smart ring and 88.3 ± 8.0% for the clinical-grade pulse oximeter, with a RMSE of 3.55%. These findings suggest that the smart ring provides reasonably accurate SpO₂ readings at rest, supporting its potential utility for health monitoring.

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Article Agriculture Food Sciences

A Study on the Effectiveness of Glycerophosphocholine (α-GPC) as an e-Sports Supplement

This study examined the effects of oral intake of Glycerophosphocholine (α-GPC) on stress response and cognitive function in e-sports. α-GPC is a precursor of acetylcholine and is sometimes used to treat Alzheimer's disease and other dementias, but its potential effects in e-sports have not been investigated. In this study, 21 participants from university e-sports clubs and other groups were given α-GPC for 2 weeks. We measured their stress responses induced by e-sports and their performance in cognitive tasks. The results showed that the placebo increased the rate of increase in salivary amylase after e-sports, but α-GPC significantly decreased this increase. It also suppressed the increase in heart rate after e-sports. Furthermore, 1g of α-GPC significantly increased the rate of correct responses in a 3-back task, a cognitive task involving working memory, after ingestion. These results suggest that 2-week intake of α-GPC may enhance cognitive function and contribute to stress reduction and suppression of autonomic nervous system disturbances caused by e-sports. Further study is needed to determine the minimum effective intake period of the supplement.

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Special Issue Information Sciences

Fundamental Study on Detection of Dangerous Objects on the Road Surface Leading to Motorcycle Accidents Using a 360-Degree Camera

In recent years, the number of fatalities in traffic accidents involving motorcyclists has remained almost unchanged, with single-vehicle accidents accounting for 37.2% of all accidents by accident type in the past five years. In the development of overturn prevention devices for motorcycles, problems remain in post-mounting of the device as well as its downsizing. On the other hand, an existing study using deep learning has proposed a method for detecting dangerous objects on the road surface leading motorcycles to overturn, though this method still needs verification under different conditions. In this study, we apply a method for detecting dangerous objects on the road surface from video images using YOLO to two types of 360-degree cameras and verify that this method is versatile under different conditions.

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Special Issue Agriculture Electrical and Electronic Engineering Food Sciences Information Sciences

Wildlife Approach Detection Using a Custom-Built Multimodal IoT Camera System with Environmental Sound Analysis

This paper presents a custom-built IoT camera system designed for recognizing wild animal approaches, where data transmission and power consumption are critical concerns in resource-constrained outdoor settings. The proposed method involves the spectral analysis on both infrared and environmental sound data before uploading images and videos to the remote server. Experiments, including battery endurance tests and wildlife monitoring, were conducted to validate the system. These results showed that the system minimized false positives caused by environmental factors such as wind or vegetation movement. Importantly, adding frequency features from audio waveforms that capture sounds including wind noise and footsteps led to an improvement in detection accuracy, which increased the AUC from 0.894 to 0.990 in Random Forest (RF) and from 0.900 with infrared sensor data alone to 0.987 in Logistic Regression (LR). These findings contribute to applications in wildlife conservation, agricultural protection, and ecosystem monitoring.

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Special Issue Information Sciences

Research on Indoor Self-Location Estimation Technique Using Similar Image Retrieval Considering Environmental Changes

In Japan, the shortage of human resources due to the declining birthrate and aging population is becoming a social problem. Particularly in the security industry, the irregular working hours and associated risks are making it increasingly challenging to secure workers. This has led to a rise in use of security systems that utilize security cameras and drones. However, in factories and other buildings with a lot of equipment and intricate structures, there is the problem of blind spots caused by occlusion. This situation necessitates the use of automated drone patrols, and a problem arises when self-position estimation fails in areas where acquiring feature points is difficult, such as corridors. To solve these problems, in a previous study, we devised a technique for position estimation using a method that can calculate similarity based on changes in the distribution of color information across the entire image. In this study, we propose a method that can cope with environmental changes caused by object movement while combining feature point-based methods.

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Special Issue Engineering in General Information Sciences Others

A Study on the Development of a Traffic Volume Counting Method by Vehicle Type and Direction Using Deep Learning

The turning movement count is investigated to understand the traffic conditions at intersections and identify bottleneck locations. In recent years, methods utilizing probe data and AI-based analysis of video images have been developed to streamline the survey process. Existing methods can count vehicles as they pass but struggle to classify vehicle types. Therefore, the objective of this study is to develop a method for counting turning movement count by vehicle type using deep learning. In this method, YOLOv8 is used to detect cars, buses, and trucks in video images, and BoT-SORT is used for tracking. When a vehicle being tracked crosses the cross-sectional lines and auxiliary lines at the intersection captured in the video images, it is counted by class. In this case, the entry direction of vehicles that cannot be determined upon entering the intersection is estimated based on accurately counted vehicles. Additionally, the entry direction is inferred from a series of vector information within the detection bounding boxes. The results of the verification experiment showed that the proposed method can count the directional traffic volume with an accuracy of over 95.0% and classify the three vehicle classes—car, bus, and truck—with an accuracy of over 90.0%.

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Special Issue Engineering in General Information Sciences Others

Detecting Near-Miss Actions and Estimating Physical Fatigue among Construction Workers Using Wearable Sensors

Labor shortages in the construction industry have become a serious issue in developed countries, particularly in Japan, where workforce aging and declining recruitment of young workers are significant challenges. In this context, ensuring worker safety has become increasingly critical. While occupational accidents in Japan's construction industry have decreased annually due to proper safety measures, the construction industry still has the highest number of fatalities among all industries. Falls from height and falls on the same level are the leading causes of injuries and fatalities. Therefore, detecting near-miss incidents (such as tripping and slipping) that precede falls, along with physical fatigue, could help prevent occupational accidents. This study investigated the feasibility of detecting near-miss incidents and estimating fatigue levels using wearable sensors suitable for continuous monitoring at construction sites. We conducted validation experiments simulating near-miss actions and fatigue conditions. Results showed that applying a Convolutional Neural Network (CNN) to data collected from an iPhone® placed in workers' trouser pockets achieved an F1-score of 0.95 in detecting near-miss actions. Additionally, by comparing body sway magnitudes before and after fatigue, we confirmed the potential for estimating physical fatigue.

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