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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) is a positive-sense singlestranded RNA (+ssRNA) that causes coronavirus disease 2019 . The viral genome encodes twelve genes for viral replication and infection. The third open reading frame is the spike (S) gene that encodes for the spike glycoprotein interacting with specific cell surface receptor -angiotensin converting enzyme 2 (ACE2) -on the host cell membrane. Most recent studies identified a single point mutation in S gene. A single point mutation in S gene leading to an amino acid substitution at codon 614 from an aspartic acid 614 into glycine (D614G) resulted in greater infectivity compared to the wild type SARS-CoV2. We were interested in investigating the mutation region of S gene of SARS-CoV2 from Korean COVID-19 patients. New mutation sites were found in the critical receptor binding domain (RBD) of S gene, which is adjacent to the aforementioned D614G mutation residue. This specific sequence data demonstrated the active progression of SARS-CoV2 by mutations in the RBD of S gene. The sequence information of new mutations is critical to the development of recombinant SARS-CoV2 spike antigens, which may be required to improve and advance the strategy against a wide range of possible SARS-CoV2 mutations.

A second cluster of COVID-19 cases imported from Europe occured in Vietnam from early March 2020. We describe 44 SARS-CoV-2 RT-PCR positive patients (cycle threshold value <30) admitted to the National Hospital for Tropical Diseases in Hanoi between March 6 and April 15 2020. Whole SARS-CoV-2 genomes from these patients were sequenced using Illumina Miseq and analysed for common genetic variants and relationships to local and globally circulating strains. Results showed that 32 cases were Vietnamese with a median age of 37 years (range 15–74 years), and 23 were male. Most cases were acquired outside Vietnam, mainly from the UK (n = 15), other European countries (n = 14), Russia (n = 6) and countries in Asia (n = 3). No cases had travelled from China. Forty-one cases had symptoms at admission, typically dry cough (n = 36), fever (n = 20), sore throat (n = 14) and diarrhoea (n = 12). Hospitalisation was long with a median of 25 days, most commonly from 20–29 days. All SARS-CoV-2 genomes were similar (92–100% sequence homology) to the reference sequence Wuhan_1 (NC_045512), and 32 strains belonged to the B.1.1 lineage. The three most common variants were linked, and included C3037T, C14408T (nsp12: P323L) and A23403G (S: D614G) mutations. This group of mutations often accompanied variant C241T (39/44 genomes) or GGG 28881..28883 AAC (33/44 genomes). The prevalence of the former reflected probable European origin of viruses, and the transition D614G was dominant in Vietnam. New variants were identified; however, none could be associated with disease severity.

In this study, we report a custom designed wireless gait analysis sensor (WGAS) system for real-time fall detection using a Support Vector Machine (SVM) classifier. Our WGAS includes a tri-axial accelerometer, 2 gyroscopes and a MSP430 micro-controller. It was worn by the subjects at either the T4 or at the waist level for various intentional falls, Activities of Daily Living (ADL) and the Dynamic Gait Index (DGI) test. The raw data of tri-axial acceleration and angular velocity is wirelessly transmitted from the WGAS to a nearby PC, and then 6 features were extracted for fall classification using a SVM (Support Vector Machine) classifier. We achieved 98.8% and 98.7% fall classification accuracies from the data at the T4 and belt positions, respectively. Moreover, the preliminary data demonstrates an impressive overall specificity of 99.5% and an overall sensitivity of 97.0% for this WGAS real-time fall detection system.

It has been the dream of many scientists and engineers to realize a non-contact remote sensing system that can perform continuous, accurate and long-term monitoring of human vital signs as we have seen in many Sci-Fi movies. Having an intelligible sensor system that can measure and record key vital signs (such as heart rates and respiration rates) remotely and continuously without touching the patients, for example, can be an invaluable tool for physicians who need to make rapid life-and-death decisions. Such a sensor system can also effectively help physicians and patients making better informed decisions when patients’ long-term vital signs data is available. Therefore, there has been a lot of research activities on developing a non-contact sensor system that can monitor a patient’s vital signs and quickly transmit the information to healthcare professionals. Doppler-based radio-frequency (RF) non-contact vital signs (NCVS) monitoring system are particularly attractive for long term vital signs monitoring because there are no wires, electrodes, wearable devices, nor any contact-based sensors involved so the subjects may not be even aware of the ubiquitous monitoring. In this paper, we will provide a brief review on some latest development on NCVS sensors and compare them against a few novel and intelligent phased-array Doppler-based RF NCVS biosensors we have built in our labs. Some of our NCVS sensor tests were performed within a clutter-free anechoic chamber to mitigate the environmental clutters, while most tests were conducted within the typical Herman-Miller type office cubicle setting to mimic a more practical monitoring environment. Additionally, we will show the measurement data to demonstrate the feasibility of long-term NCVS monitoring. The measured data strongly suggests that our latest phased array NCVS system should be able to perform long-term vital signs monitoring intelligently and robustly, especially for situations where the subject is sleeping without hectic movements nearby.

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Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU) was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI) tests while wearing the custom wireless gait analysis sensor (WGAS). The small WGAS includes a tri-axial accelerometer integrated circuit (IC), two gyroscopes ICs and a Texas Instruments (TI) MSP430 microcontroller and is worn by each subject at the T4 position during the DGI tests. The raw gait data are wirelessly transmitted from the WGAS to a near-by PC for real-time gait data collection and analysis. In order to perform successful classification of patients vs. normal subjects, we used several different classification algorithms, such as the back propagation artificial neural network (BP-ANN), support vector machine (SVM), k-nearest neighbors (KNN) and binary decision trees (BDT), based on features extracted from the raw gait data of the gyroscopes and accelerometers. When the range was used as the input feature, the overall classification accuracy obtained is 100% with BP-ANN, 98% with SVM, 96% with KNN and 94% using BDT. Similar high classification accuracy results were also achieved when the standard deviation or other values were used as input features to these classifiers. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real time using various classifiers, the success of which may eventually lead to accurate and objective diagnosis of abnormal human gaits and their underlying etiologies in the future, as more patient data are being collected.

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