Comparison of the effects of indoor and outdoor exercise on creativity: an analysis of EEG alpha power.

Previous research finds that natural environments and exercise enhance creativity. In this within-subjects design study, we examined the influence of outdoor exercise that combined a natural environment with exercise on creativity compared to an indoor exercise control condition by analyzing cognitive activities related to creativity. The participants performed an Alternative Uses Test (AUT), in which ordinary objects are presented to the participants (e.g., a brick), to prompt as many ideas for alternative uses as possible, which are transformed into a creativity score, after indoor running and outdoor running. During the test, brain activity was recorded using electroencephalography (EEG) and a short version flow state scale (FSS) was completed after the experiment. Results showed that while AUT scores did not significantly differ between conditions, alpha band activity at the parietal occipital region involved in divergent creativity increased during the AUT after outdoor exercise while it did not during the AUT after indoor exercise. In addition, FSS scores for positive emotional experience and absorption were higher after outdoor exercise than after indoor exercise. Our results from the FSS suggest that exercise in a natural environment is perceived subjectively differently from indoor exercise, participants report greater experiences of flow compared to indoor exercise, and the EEG measures objectively indicate enhanced cognitive activity in a creativity task after outdoor exercise. This study suggests that outdoor exercise increases neuronal activity in brain regions related to creativity. Further research is needed to understand how this can lead to increased creativity.

Criteria for detection of possible risk factors for mental health problems in undergraduate university students.

Introduction: Developing approaches for early detection of possible risk clusters for mental health problems among undergraduate university students is warranted to reduce the duration of untreated illness (DUI). However, little is known about indicators of need for care by others. Herein, we aimed to clarify the specific value of study engagement and lifestyle habit variables in predicting potentially high-risk cluster of mental health problems among undergraduate university students. Methods: This cross-sectional study used a web-based demographic questionnaire [the Utrecht Work Engagement Scale for Students (UWES-S-J)] as study engagement scale. Moreover, information regarding life habits such as sleep duration and meal frequency, along with mental health problems such as depression and fatigue were also collected. Students with both mental health problems were classified as high risk. Characteristics of students in the two groups were compared. Univariate logistic regression was performed to identify predictors of membership. Receiver Operating Characteristic (ROC) curve was used to clarify the specific values that differentiated the groups in terms of significant predictors in univariate logistic analysis. Cut-off point was calculated using Youden index. Statistical significance was set at p < 0.05. Results: A total of 1,644 students were assessed, and 30.1% were classified as high-risk for mental health problems. Significant differences were found between the two groups in terms of sex, age, study engagement, weekday sleep duration, and meal frequency. In the ROC curve, students who had lower study engagement with UWES-S-J score < 37.5 points (sensitivity, 81.5%; specificity, 38.0%), <6 h sleep duration on weekdays (sensitivity, 82.0%; specificity, 24.0%), and < 2.5 times of meals per day (sensitivity, 73.3%; specificity, 35.8%), were more likely to be classified into the high-risk group for mental health problems. Conclusion: Academic staff should detect students who meet these criteria at the earliest and provide mental health support to reduce DUI among undergraduate university students.

A method for improving semantic segmentation using thermographic images in infants.

BACKGROUND: Regulation of temperature is clinically important in the care of neonates because it has a significant impact on prognosis. Although probes that make contact with the skin are widely used to monitor temperature and provide spot central and peripheral temperature information, they do not provide details of the temperature distribution around the body. Although it is possible to obtain detailed temperature distributions using multiple probes, this is not clinically practical. Thermographic techniques have been reported for measurement of temperature distribution in infants. However, as these methods require manual selection of the regions of interest (ROIs), they are not suitable for introduction into clinical settings in hospitals. Here, we describe a method for segmentation of thermal images that enables continuous quantitative contactless monitoring of the temperature distribution over the whole body of neonates. METHODS: The semantic segmentation method, U-Net, was applied to thermal images of infants. The optimal combination of Weight Normalization, Group Normalization, and Flexible Rectified Linear Unit (FReLU) was evaluated. U-Net Generative Adversarial Network (U-Net GAN) was applied to thermal images, and a Self-Attention (SA) module was finally applied to U-Net GAN (U-Net GAN + SA) to improve precision. The semantic segmentation performance of these methods was evaluated. RESULTS: The optimal semantic segmentation performance was obtained with application of FReLU and Group Normalization to U-Net, showing accuracy of 92.9% and Mean Intersection over Union (mIoU) of 64.5%. U-Net GAN improved the performance, yielding accuracy of 93.3% and mIoU of 66.9%, and U-Net GAN + SA showed further improvement with accuracy of 93.5% and mIoU of 70.4%. CONCLUSIONS: FReLU and Group Normalization are appropriate semantic segmentation methods for application to neonatal thermal images. U-Net GAN and U-Net GAN + SA significantly improved the mIoU of segmentation.

Characteristics of Adaptation in Undergraduate University Students Suddenly Exposed to Fully Online Education During the COVID-19 Pandemic.

This study aimed to clarify the adaptation features of University students exposed to fully online education during the novel coronavirus disease 2019 (COVID-19) pandemic and to identify accompanying mental health problems and predictors of school adaptation. The pandemic has forced many universities to transition rapidly to delivering online education. However, little is known about the impact of this drastic change on students' school adaptation. This cross-sectional study used an online questionnaire, including assessments of impressions of online education, study engagement, mental health, and lifestyle habits. In total, 1,259 students were assessed. The characteristics of school adaptation were analyzed by a two-step cluster analysis. The proportion of mental health problems was compared among different groups based on a cluster analysis. A logistic regression analysis was used to identify predictors of cluster membership. P-values < 0.05 were considered statistically significant. The two-step cluster analysis determined three clusters: school adaptation group, school maladaptation group, and school over-adaptation group. The last group significantly exhibited the most mental health problems. Membership of this group was significantly associated with being female (OR = 1.42; 95% CI 1.06-1.91), being older (OR = 1.21; 95% CI 1.01-1.44), those who considered online education to be less beneficial (OR = 2.17; 95% CI 1.64-2.88), shorter sleep time on weekdays (OR = 0.826; 95% CI 0.683-.998), longer sleep time on holidays (OR = 1.21; 95% CI 1.03-1.43), and worse restorative sleep (OR = 2.27; 95% CI 1.81-2.86). The results suggest that academic staff should understand distinctive features of school adaptation owing to the rapid transition of the educational system and should develop support systems to improve students' mental health. They should consider ways to incorporate online classes with their lectures to improve students' perceived benefits of online education. Additionally, educational guidance on lifestyle, such as sleep hygiene, may be necessary.

Successful Reboot of High-Performance Sporting Activities by Japanese National Women's Handball Team in Tokyo, 2020 during the COVID-19 Pandemic: An Initiative Using the Japan Sports-Cyber Physical System (JS-CPS) of the Sports Research Innovation Project (SRIP).

The COVID-19 pandemic has negatively impacted sporting activities across the world. However, practical training strategies for athletes to reduce the risk of infection during the pandemic have not been definitively studied. The purpose of this report was to provide an overview of the challenges we encountered during the reboot of high-performance sporting activities of the Japanese national handball team during the 3rd wave of the COVID-19 pandemic in Tokyo, Japan. Twenty-nine Japanese national women's handball players and 24 staff participated in the study. To initiate the reboot of their first training camp after COVID-19 stay-home social policy, we conducted: web-based health-monitoring, SARS-CoV-2 screening with polymerase chain reaction (PCR) tests, real-time automated quantitative monitoring of social distancing on court using a moving image-based artificial intelligence (AI) algorithm, physical intensity evaluation with wearable heart rate (HR) and acceleration sensors, and a self-reported online questionnaire. The training camp was conducted successfully with no COVID-19 infections. The web-based health monitoring and the frequent PCR testing with short turnaround times contributed remarkably to early detection of athletes' health problems and to risk screening. During handball, AI-based on-court social-distance monitoring revealed key time-dependent spatial metrics to define player-to-player proximity. This information facilitated appropriate on- and off-game distancing behavior for teammates. Athletes regularly achieved around 80% of maximum HR during training, indicating anticipated improvements in achieving their physical intensities. Self-reported questionnaires related to the COVID management in the training camp revealed a sense of security among the athletes that allowed them to focus singularly on their training. The challenges discussed herein provided us considerable knowledge about creating and managing a safe environment for high-performing athletes in the COVID-19 pandemic via the Japan Sports-Cyber Physical System (JS-CPS) of the Sports Research Innovation Project (SRIP, Japan Sports Agency, Tokyo, Japan). This report is envisioned to provide informed decisions to coaches, trainers, policymakers from the sports federations in creating targeted, infection-free, sporting and training environments.

Drone-Based Water Level Detection in Flood Disasters.

Japan was hit by typhoon Hagibis, which came with torrential rains submerging almost eight-thousand buildings. For fast alleviation of and recovery from flood damage, a quick, broad, and accurate assessment of the damage situation is required. Image analysis provides a much more feasible alternative than on-site sensors due to their installation and maintenance costs. Nevertheless, most state-of-art research relies on only ground-level images that are inevitably limited in their field of vision. This paper presents a water level detection system based on aerial drone-based image recognition. The system applies the R-CNN learning model together with a novel labeling method on the reference objects, including houses and cars. The proposed system tackles the challenges of the limited and wild data set of flood images from the top view with data augmentation and transfer-learning overlaying Mask R-CNN for the object recognition model. Additionally, the VGG16 network is employed for water level detection purposes. We evaluated the proposed system on realistic images captured at disaster time. Preliminary results show that the system can achieve a detection accuracy of submerged objects of 73.42% with as low as only 21.43 cm error in estimating the water level.

ThermalWrist: Smartphone Thermal Camera Correction Using a Wristband Sensor †.

Thermal images are widely used for various healthcare applications and advanced research. However, thermal images captured by smartphone thermal cameras are not accurate for monitoring human body temperature due to the small body that is vulnerable to temperature change. In this paper, we propose ThermalWrist, a dynamic offset correction method for thermal images captured by smartphone thermal cameras. We fully utilize the characteristic that is specific to thermal cameras: the relative temperatures in a single thermal image are highly reliable, although the absolute temperatures fluctuate frequently. To correct the offset error, ThermalWrist combines thermal images with a reliable absolute temperature obtained by a wristband sensor based on the above characteristic. The evaluation results in an indoor air-conditioned environment shows that the mean absolute error and the standard deviation of face temperature measurement error decrease by 49.4% and 64.9%, respectively. In addition, Pearson's correlation coefficient increases by 112%, highlighting the effectiveness of ThermalWrist. We also investigate the limitation with respect to the ambient temperature where ThermalWrist works effectively. The result shows ThermalWrist works well in the normal office environment, which is 22.91 °C and above.