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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Article Information Sciences Others Psychology and Education Sociology

Clarifying the Sharpened network diversity in French flair rugby

This study aimed that open rugby, known as flair rugby, drives the modern game by analyzing the 2022-2023 international test matches of France representative team. We examined the superiority of a spatial tactic called French flair rugby. First, the advantage of creating a relatively large number of networks was demonstrated. From the transitivity analysis of the network (CUG test; Conditional Uniform Graph test), the cooperation occurs at a higher level than in other networks. The network graph structure showed which players functioned centrally at which time of match as unusual positions, i.e., multi-position and multi-skill. In this study, we operationally defined this diversity as the sum of the standardized eigenvector centralities. We found that the increase in the time-series score balance tended to reduce and sharpened the diversity. As a result of examining a scale-free model in network theory, Sharpening the diversity (central and transitive role players) tended of the network power law scaling.

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Special Issue Information Sciences

Research for Supporting Tactical Analysis Concerning Pass Skeleton in American Football

The authors have been conducting researches on the measurement and analysis of athletes, things and events in field sports. In particular, we focused on American Football which is the most intelligent and complicated sports, and carried out the possibility of matchup analysis of its pass play. Based on the past researches, it was found that it was possible to estimate success or failure for each play by applying to the time-series trajectory image data of pass matchup players using the CNN（Convolutional Neural Network）. However, it is necessary to improve versatility by supporting pass skeletons considering not only one-on-one defense but also formation of zone defense that are close to the match format. Therefore, we aim to support the analysis of pass skeletons, which had to consider huge parameters such as the position and movement of each player from the start of play to the success or failure of the pass. The research was carried out based on a hypothesis that the success or failure of a pass from the position several seconds before the origin of the QB pass throw could be determined by taking into consideration the skill that is the compatibility of each player, the positions and the movement trajectory of the receiver team（WR, TE, and RB）, and the defenders（LB，DB，S）that mark them. As a result, the success rate and failure rate of the assumed pass player could be predicted by using the position and its trajectory image of each player from 3 seconds before the pass pitch. And then, by determining that the pass to the maximum likelihood receiver is optimal, we confirmed that useful information can be provided to support strategy planning during the game and guidance during practice.

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Article Information Sciences

Measurement of Motor-vehicle Traffic Volume Using Camera Images and Artificial Intelligence

In Japan, road traffic censuses are conducted to assess road traffic conditions. Recently, techniques for counting traffic volume from video images have been attracting considerable attention in order to improve work efficiency and save labor, and a large number of technologies have been developed. However, since traffic volume surveys are often conducted 24 hours a day, day and night, at various sites and under various weather conditions, existing technologies have yet to reach the counting accuracy required in practice. The authors aim to develop techniques for traffic volume surveys applicable in practice by applying artificial intelligence. This paper reports the results of a case study in which the proposed techniques were applied to the video taken during actual traffic volume surveys.

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Technical Article Information Sciences Others

3D Point Studio: Utilization Platform for Point Cloud Data

As Laser measurement technology has made remarkable progress in recent years and the means of measuring the three-dimensional shapes of road spaces as point cloud data have diversified, point cloud data has been measured and accumulated throughout Japan. Point cloud data is useful as a means of accurately grasping the present shape and is expected to be utilized for a wide range of purposes with i-Construction as a turning point. Existing efforts have promoted development of new technologies and open data, steadily increasing opportunities to use point cloud data. However, it is difficult to use point cloud data wisely in accordance with its intended purpose because it is merely a vast set of points that indicate locations in space and does not hold information about the features indicated by the points or about their relationships to other points. Therefore, it is essential to develop an environment for utilizing point cloud data. In this study, we develop 3D Point Studio, a platform for utilizing and sharing 3D data including point cloud data by utilizing area data. The usefulness of this research will be confirmed through examples of applications in the actual sites, and its future development will be discussed.

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Article Biology Environmental Sciences General Medicine Life Sciences and Basic Medicine Social Medicine

Relationship between physical education in the first year of university and exercise habits and physical and mental health in the following spring

The present study examined the relationship between taking physical education and health-related courses in the first year of university and the acquisition and maintenance of exercise habits and physical and mental health in the student’s second year. The study population consisted of university students was 2,483 in FY2017 and 2,352 in FY2018. Of these, the 2,293 students (1,744 men, 18.5 ± 1.0 years old; 549 women, 18.6 ± 1.0 years old) who responded to the self-administered questionnaire survey on daily living habits in both years were included in the analysis. The results showed that taking physical education and health-related courses in the first year was significantly related to exercise habits the following spring. The results also suggested that these courses were more strongly related to mental health than physical health. In addition, men were significantly more likely to acquire a new exercise habit and had more improved mental health. In the future, it is necessary to clarify the causal relationship between taking physical education and health-related courses and exercise habits and the maintenance and acquisition of physical and mental health, in addition to developing effective support methods for students who dislike gender differences and exercise.

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Article Electrical and Electronic Engineering Interdisciplinary Sciences

Heart Rate Variability Indices May Change Accompanying Cognitive Skills Improvement in eSports Tasks

Electronic sports (eSports) is becoming an increasingly popular subject of research with progress in the video game industry. However, the relationship between eSports and cognitive skills and heart rate variability (HRV) indices is not fully understood. Therefore, in this study, we analyzed changes in HRV indices in 20 healthy adult men while playing eSports and evaluated improvement in their cognitive skills before and after playing eSports using the Stroop test. The subjects were divided into two groups: 10 subjects who were trained in eSports for at least 1 hour a day, 5 days a week for 6 weeks, and 10 subjects who were not trained. The results indicated that subjects in the training group tended to have improved cognitive skills. In addition, in the group that temporarily improved their cognitive skills by playing eSports, similar changes were observed in HRV indices during eSports play, suggesting a parasympathetic nervous system dominance. Thus, it is suggested that the observed HRV changes were accompanied by the temporary improvement in cognitive skills induced by eSports tasks.

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