Insomnia and excessive daytime sleepiness (EDS) are the most common complaints in sleep clinics, and the cost of healthcare services associated with them have also increased significantly. Though the brief questionnaires such as the Insomnia Severity Index (ISI) and Epworth Sleepiness Scale (ESS) can be useful to assess insomnia and EDS, there are some limitations to apply for large numbers of patients. As the researches using the Internet of Things technology become more common, the need for the simplification of sleep questionnaires has been also growing. We aimed to simplify ISI and ESS using machine learning algorithms and deep neural networks with attention models. The medical records of 1,241 patients who examined polysomnography for insomnia or EDS were analyzed. All patients are classified into five groups according to the severity of insomnia and EDS. To develop the model, six machine learning algorithms were firstly applied. After going through normalization, the process with the CNN+ Attention model was applied. We classified a group with an accuracy of 93% even with only the results of 6 items (ISI1a, ISI1b, ISI3, ISI5, ESS4, ESS7). We simplified the sleep questionnaires with maintaining high accuracy by using machine learning models.
The COVID-19 pandemic heavily influenced human life by constricting human social activity. Following the spread of the pandemic, humans did not have a choice but to change their lifestyles. There has been much change in the field of education, which has led to schools hosting online classes as an alternative to face-to-face classes. However, the concentration level is lowered in the online learning class, and the student’s learning rate decreases. We devise a framework for recognizing and estimating students’ concentration levels to help lecturers. Previous studies have a limitation in that they classified attention levels using only discrete states. Due to the partial information from discrete states, the concentration levels could not be recognized well. This research aims to estimate more subtle levels as specified states by using a minimum amount of body movement data. The deep neural network is used to continuously recognize the human concentration model, and the concentration levels can be predicted and estimated by the Kalman filter. Using our framework, we successfully extracted the concentration levels, which can aid lecturers and can be expanded to other areas. To implement the framework, we recruited participants to take online classes. Data were collected and preprocessed using pose points, and an accuracy of 90.62 % was calculated by predicting the concentration level using the framework. Furthermore, the concentration level was approximated based on the Kalman filter. We found that webcams can be used to quantitatively measure student concentration when conducting online classes. Our framework is a great help for instructors to measure concentration levels, which can increase the learning efficiency. As a future work of this study, if emotion data and skin thermal data are comprehensively considered, a student’s concentration level can be measured more precisely.
The J-PARC muon g-2/EDM experiment is a new experiment to measure the anomalous magnetic moment (g-2) and electric dipole moment (EDM) of muon at J-PARC. The experiment is planned to start in 2016, and targets a sensitivity of 0.1 parts per million (ppm). In the muon storage ring, it is required to convert a high direct current (DC) voltage to ∼1.5 V of DC voltage values to provide bias voltage to readout electronics. For the purpose, we are developing a step-down DC-to-DC converter utilizing a Buck converter. To capture the muons produced in the experiment, the detector components inside the muon storage ring are under 3 T magnetic field. The converter may contain components that produce additional magnetic fields, and those fields have to be lower than 30 µT to reach its physics goal. In addition to the magnetic field, the experiment is quite sensitive to the electric field in the storage ring; therefore, the electric field produced by the converter has to be minimal as well (E 1 V/m). In this paper, we present the development status of our DC-to-DC converter for the J-PARC muon g-2/EDM experiment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.