Noncontact Respiratory Measurement for Multiple People at Arbitrary Locations Using Array Radar and Respiratory-Space Clustering

We developed a noncontact measurement system for monitoring the respiration of multiple people using millimeter-wave array radar. To separate the radar echoes of multiple people, conventional techniques cluster the radar echoes in the time, frequency, or spatial domain. Focusing on the measurement of the respiratory signals of multiple people, we propose a method called respiratory-space clustering, in which individual differences in the respiratory rate are effectively exploited to accurately resolve the echoes from human bodies. The proposed respiratory-space clustering can separate echoes, even when people are located close to each other. In addition, the proposed method can be applied when the number of targets is unknown and can accurately estimate the number and positions of people. We perform multiple experiments involving five or seven participants to verify the performance of the proposed method, and quantitatively evaluate the estimation accuracy for the number of people and the respiratory intervals. The experimental results show that the average root-mean-square error in estimating the respiratory interval is 196 ms using the proposed method. The use of the proposed method, rather the conventional method, improves the accuracy of the estimation of the number of people by 85.0%, which indicates the effectiveness of the proposed method for the measurement of the respiration of multiple people

Cottage Industry and Organizational Transitions of the Toyama Pharmacy “Kokando” in the Meiji Era

departmental bulletin pape

Pharmaceutical Affairs Laws in the Meiji and Taisho Eras and the Toyama Pharmacy “Kokando”

departmental bulletin pape

Radar-Based Automatic Detection of Sleep Apnea Using Support Vector Machine

2020 International Symposium on Antennas and Propagation (ISAP), 25-28 Jan. 2021, Osaka, JapanEarly diagnosis of sleep-apnea-related breathing problems helps to avoid the increased risk they can cause. In this study, we performed simultaneous radar measurements and polysomnography on patients with sleep apnea. A support vector machine algorithm was applied to the radar data to automatically detect sleep apnea events. Support vector machine parameters were optimized using the relationship between the radar and polysomnography data. The support vector machine was found to be effective in noncontact detection of central/mixed sleep apnea events using radar data. The proposed approach achieved an accuracy of 79.5%, a recall of 71.2%, and a precision of 71.2%

Noncontact Detection of Sleep Apnea Using Radar and Expectation-Maximization Algorithm

Sleep apnea syndrome requires early diagnosis because this syndrome can lead to a variety of health problems. If sleep apnea events can be detected in a noncontact manner using radar, we can then avoid the discomfort caused by the contact-type sensors that are used in conventional polysomnography. This study proposes a novel radar-based method for accurate detection of sleep apnea events. The proposed method uses the expectation-maximization algorithm to extract the respiratory features that form normal and abnormal breathing patterns, resulting in an adaptive apnea detection capability without any requirement for empirical parameters. We conducted an experimental quantitative evaluation of the proposed method by performing polysomnography and radar measurements simultaneously in five patients with the symptoms of sleep apnea syndrome. Through these experiments, we show that the proposed method can detect the number of apnea and hypopnea events per hour with an error of 4.8 times/hour; this represents an improvement in the accuracy by 1.8 times when compared with the conventional threshold-based method and demonstrates the effectiveness of our proposed method.Comment: 8 pages, 12 figures, 3 tables. This work is going to be submitted to the IEEE for possible publicatio

Gambling symptoms, behaviors, and cognitive distortions in Japanese university students

Background: This study was conducted to investigate the relationship between symptoms of gambling problems, gambling behaviours, and cognitive distortions among a university student population in Japan ages 20 to 29 years. We aimed to address the gap in knowledge of gambling disorders and treatment for this population. Methods: Data were obtained from 1471 Japanese undergraduate students from 19 universities in Japan. Descriptive statistics and hierarchical multivariate regression analysis were used to investigate whether the factors of gambling cognitive distortions would have predictive effects on gambling disorder symptoms. Results: Results indicated that 5.1% of the participants are classifiable as probable disordered gamblers. The bias of the gambling type to pachinko and pachislot was unique to gamblers in Japan. Of the students sampled, 342 self-reported gambling symptoms via the South Oaks Gambling Screen. Hierarchical multivariate regression analysis indicated that one domain of gambling cognitive distortions was associated significantly with gambling symptoms among the 342 symptomatic participants: gambling expectancy (β = 0.19, p < .05). The multivariate model explained 47% of the variance in the gambling symptoms. Conclusion: This study successfully contributed to the sparse research on university student gambling in Japan. Specifically, our results indicated a statistically significant relationship between gambling cognitive distortions and gambling disorder symptoms. These results can inform the development of preventive education and treatment for university students with gambling disorder in Japan. The report also describes needs for future research of university students with gambling disorder