ABSTRACT
Around the beginning of April 2021, the Thai Covid-19 Alpha-Delta wave began to spread. After approximately 37 weeks, the Omicron wave emerged in mid-December 2021. This paper proposes a Fourier transform approach for examining the exact wave breaks and reveals the hidden information in the power spectrum of the pandemic by converting the number of infected cases, death cases, and recovered cases caused by Covid-19 in Thailand from the time domain to the frequency domain. Analyses are conducted on wave intensity, pattern, and cycle duration. Two validation procedures are proposed to ensure the correct identification of wave breaks. The first strategy employs cross-correlation, whereas the second technique utilizes matching peak frequencies. Our results mathematically determined the Alpha-Delta wave's end date and the Omicron wave's beginning date. The matched peak frequency between the waves was discovered at frequency 5.286, corresponding to a cycle length of 7.027 days. The outcome enables policymakers to comprehend the pandemic's trend and determine if the policy should be strengthened or relaxed. © 2022 IEEE.
ABSTRACT
The 24-h breathing patterns may be closely related to health status as well as disease progression. However, there is no consistent and widely accepted approach for mining the potential value in 24-h respiratory signals based on wearable device monitoring. This study presented a reference approach including signal quality assessment, calibration of tidal volume, and breathing patterns parameters based on a wearable continuous physiological parameter monitoring system for 24-h breathing patterns analysis, including time domain, frequency domain and nonlinear domain. 70 healthy subjects and 76 patients undergoing heart valve surgery were enrolled in this study. The normal reference range of breathing patterns was calculated based on healthy subjects. A subgroup study was conducted based on whether patients developed postoperative pulmonary complications (PPCs). Compared with non-PPCs group, the coefficient of variation of breathing rate in the recumbent position was smaller in the PPCs group. During the daytime, the kurtosis of breathing rate and contribution of the abdomen was smaller in PPCs group. During the nighttime, the coefficient of variation of breathing rate and SD2 was smaller in the PPCs group. The quantitative method proposed in this study fills the gap in the field of quantifying 24-h breathing patterns which is effective in discriminating different populations and is expected to be used widely in the context of COVID-19 epidemic. © 2022 IEEE.
ABSTRACT
Short stick exercises have been attracting attention from the viewpoint of preventing falls and improving the health of elderly people and are generally performed under the guidance of instructors and nursing staff at nursing homes. However, in situations such as the COVID-19 pandemic, where people should refrain from unnecessary outings, it is advisable that individuals perform short stick exercises at home and record their exercise implementation status. In this paper, we propose an inertial measurement unit (IMU)-sensor-based short stick exercise tracking method that can automatically record the types and amounts of exercises performed using a short stick equipped with an IMU sensor. The proposed method extracts time-domain and frequency-domain features from linear acceleration and quaternion time-series data obtained from the IMU sensor and classifies the type of exercise using an inference model based on machine learning algorithms. To evaluate the proposed method, we collected sensor data from 21 young subjects (in their 20s) and 14 elderly subjects (79-95 years old), where the participants performed three sets (10 times per set) of eight basic types of short stick exercises (five types for elderly people). As a result of evaluating the proposed method using this data set, we confirmed that when LightGBM was used as the learning algorithm, it achieved F values of 90.0 and 86.6% for recognizing the type of exercise for young and elderly people, respectively. © 2022 M Y U Scientific Publishing Division. All rights reserved.
ABSTRACT
Spectral properties of S and S1 spike proteins were studied using vibration spectroscopy methods. Also, vibration signatures of amino acids that make up the RGD, the part of S1 protein responsible for adhesion, were studied by Terahertz (THz) time-domain, Infrared (IR) and Raman spectroscopies in the low-frequency and in the fingerprint. THz metamaterials have been developed that increase the sensitivity of THz spectroscopy to the presence of both proteins. We have studied the spectra of amino acids, which play a role in the adhesion of proteins, on the metal surfaces of metamaterials, aluminum, and gold. Spectral differences were found between S and S1 proteins of SARS-CoV-2. The spectral differences between amino acids prepared in the form of thin films and in the form of bulk sample are discussed. © 2021 IEEE