The widespread, ongoing COVID-19 pandemic has brought to the fore concerns regarding the psychological well-being of people. Recent research revealed various issues impacting mental health of people. However, a systematic study of the emotional drift of the populace, has been precluded so far. Our investigative research seeks to explore stress factors for different subgroups in India, variation in primary emotions during COVID-19 initial phase, and the emotional impact of activities practiced by people to adjust to the new norms. We conduct an online questionnaire-based survey that elicits responses from 958 participants. Our analysis establishes significant correlations between pandemic-induced causative factors and stresses in subgroups and micro-community. Unexpected events during the pandemic disturbed community's emotional equilibrium. Lastly, we find specific activities demonstrating an ameliorative impact on the emotional well-being of people. Our analysis emphasizes the need for a pre-planned infrastructure to provide Psychological First Aid (PFA) to foster psychological preparedness.

The COVID-19 pandemic spread all over the world, starting in China in late 2019, and significantly affected life in all aspects. As seen in SARS, MERS, COVID-19 outbreaks, coronaviruses pose a great threat to world health. The COVID-19 epidemic, which caused pandemics all over the world, continues to seriously threaten people's lives. Due to the rapid spread of COVID-19, many countries' healthcare sectors were caught off guard. This situation put a burden on doctors and healthcare professionals that they could not handle. All of the studies on COVID-19 in the literature have been done to help experts to recognize COVID-19 more accurately, to use more accurate diagnosis and appropriate treatment methods. The alleviation of this workload will be possible by developing computer aided early and accurate diagnosis systems with machine learning. Diagnosis and evaluation of pneumonia on computed tomography images provide significant benefits in investigating possible complications and in case follow-up. Pneumonia and lesions occurring in the lungs should be carefully examined as it helps in the diagnostic process during the pandemic period. For this reason, the first diagnosis and medications are very important to prevent the disease from progressing. In this study, a dataset consisting of Pneumonia and Normal images was used by proposing a new image preprocessing process. These preprocessed images were reduced to 15x15 unit size and their features were extracted according to their RGB values. Experimental studies were carried out by performing both normal values and feature reduction among these features. RGB values of the images were used in train and test processes for MLAs. In experimental studies, 5 different Machine Learning Algorithms (MLAs) (Multi Class Support Vector Machine (MC-SVM), k Nearest Neighbor (k-NN), Decision Tree (DT), Multinominal Logistic Regression (MLR), Naive Bayes (NB)). The following accuracy rates were obtained in train operations for MLAs, respectively; 1, 1, 1, 0.746377, 0.963768. Accuracy results in test operations were obtained as follows; 0.87755, 0.857143, 0.857143, 0.877551, 0.938776.

In this research, blood samples of 47 patients infected by COVID were analyzed. The samples were taken on the 1st, 3rd and 6th month after the detection of COVID infection. Total antibody levels were measured against the SARS-CoV-2 N antigen and surrogate virus neutralization by serological methods. To differentiate COVID patients with different antibody levels, Fourier Transform InfraRed (FTIR) and Raman spectroscopy methods were used. The spectroscopy data were analyzed by multivariate analysis, machine learning and neural network methods. It was shown, that analysis of serum using the above-mentioned spectroscopy methods allows to differentiate antibody levels between 1 and 6 months via spectral biomarkers of amides II and I. Moreover, multivariate analysis showed, that using Raman spectroscopy in the range between 1317 cm and 1432 cm, 2840 cm and 2956 cm it is possible to distinguish patients after 1, 3, and 6 months from COVID with a sensitivity close to 100%.

Locating individuals within a space has numerous potential uses within a smart environment. Many different technologies have been explored towards this end though privacy-concerns, the need to wear a device, and/or extensive building modifications have presented challenges towards adoption. Structural vibrations caused by footsteps have been shown to overcome these challenges though current methods rely on time-of-flight variations. This paper presents the use of the Force Estimation and Event Localization (FEEL) Algorithm that utilizes lower sampling rates, less sensors, etc than time-of-flight methods towards locating persons. Improvements to FEEL's force estimation and SDFE localization method are additionally presented with demonstrated increases in accuracy. Analysis of 1100 footsteps resulted in 98.6% localization accuracy using the improved FEEL. Ground force reactions (GRF) were estimated by FEEL and used to estimate participant body-weight-ratios (BWR). Estimated BWRs were within ranges reported in previous works for both barefoot and shoe cases.

Today COVID-19 pandemic articulates high stress on clinical resources around the world. At present, physical and viral tests are slowly emerging, and there is a need for robust pandemic detection that biomedical sensors can aid. The utility of biomedical sensors is correlated with the medical instruments with physiological metrics. These Biomedical sensors are integrated with the systematic device to track the target analytes with a biomedical component. The COVID-19 patients' samples are collected, and biomarkers are detected using four sensors: blood pressure sensor, G-FET based biosensor, electrochemical sensor, and potentiometric sensor with different quantifiable measures. The imputed data is then profiled with chest X-ray images from the Covid-19 patients.Multi-Layer Perceptron (MLP), an AI model, is deployed to identify the hidden signatures with biomarkers. The performance of the biosensor is measured with three parameters such as sensitivity, specificity and detection limit by generating the calibration plots that accurately fits the model.

Measuring gait parameters (e.g. speed, cadence, step duration) accurately is invaluable for evaluation during treatment of older adults who struggle with disability onset, disease progression, balance, and injurious falls. Traditionally stopwatches or timing gates are used to measure gait speed in clinical settings, and these are limited to measuring gait speed. Other wearable and non-wearable technologies offer the ability to measure additional gait parameters though patients are known to walk differently with the devices and even tend to slow down before engaging with a non-wearable such as a floor mat. Floor vibrations are a promising option to measuring gait parameters while not being intrusive and not requiring line-of-sight to the patient for measurements. This paper presents methodology for extracting gait parameters using vibrations with comparisons to APDM Wearable Technologies Mobility Lab sensors and stopwatch measurements. Performance is examined across 97 participants for self-selected speed forward, full speed forward, and backwards walks at three different testing sites for a total of 1039 walks. Gait speed vibrations measurements demonstrated excellent reliability with APDM Mobility Lab (ICC: 0.98; 99% CI: 0.01±0.01 m/s) and stopwatch (ICC: 0.97; 99% CI: -0.01±0.01 m/s) measurements. Similar excellent results are reported for cadence, gait cycle duration, step duration, and stride length parameters.

Smartphone applications are increasingly being used to measure gait due to their portability and cost-effectiveness. Important reliability metrics are not available for most of these devices. The purpose of this article was to evaluate the test-retest reliability and concurrent validity of spatiotemporal gait using the novel Gait Analyzer smartphone application compared to the Tekscan Strideway. Healthy participants (n=23) completed 12 trials of 10-meter walking, at two separate time points, using Gait Analyzer and while walking across the Tekscan Strideway. The results suggest excellent test-retest reliability for the Gait Analyzer and good test-retest reliability for the Tekscan Strideway for both velocity and cadence. At both time points, these devices were moderately to strongly correlated to one another for both velocity and cadence. These data suggest that the Gait Analyzer and Tekscan Strideway are reliable over time and can comparably calculate velocity and cadence.

We extend the use of the intrinsic properties calibration method for triaxial accelerometers that we reported previously from discrete angular steps to using a constant rotation rate to produce a time varying sinusoidal excitation in the earth's gravitational field. We show that this extension yields the low frequency calibration response of the device under test. Whereas traditional vibration-based methods using shakers generally exhibit an increased measurement uncertainty with decreased excitation frequency, we show that this approach does not. We report results obtained from a commercial triaxial digital accelerometer from DC up to a 0.5 Hz rotation rate. The maximum rotation rate that we report is limited by our rotation stage; but we expect that the method can be extended to higher rotation rates with an upper limit constrained by what can be tolerated as a maximum centripetal acceleration.

Information about blood arterial oxygen saturation (SpO) is crucial in critical care settings or home health monitoring during the COVID-19 pandemic. Also, we need to identify the factors that affect the SpO measurement. In this paper, the effect of compression of the cuff during noninvasive blood pressure (NIBP) measurement on the SpO results was investigated. A custom-made system was used for simultaneous measurement of NIBP and SpO. The study was conducted on 213 subjects aged between 21 and 93, with a systolic blood pressure of (94 to 194) mmHg, diastolic blood pressure of (52-98) mmHg, and 994 NIBP readings were used for the analysis. During the NIBP measurement, momentary changes in SpO can reach ±17% and are in most cases positive (mean 2.9%). The change was not correlated with sex, age, height, body weight, BMI, HR and blood pressure. The obtained results show that frequent NIBP measurements may lead to wrong conclusions about SpO. In our study, pressure measurements mainly caused the increase of blood oxygenation level.

Biomedical images contain a large volume of sensor measurements, which can reveal the descriptors of the disease under investigation. Computer-based analysis of such measurements helps detect the disease, and thereby swiftly aid medical professionals to choose adequate therapy. In this paper, we propose a robust deep learning ensemble framework known as COVID Fuzzy Ensemble Network, or COFE-Net. This strategy is proposed for the task of COVID-19 screening from chest X-rays (CXR) and CT Scans, as a part of Computer-Aided Detection (CADe) for medical practitioners. We leverage the strategy of Transfer Learning for Convolutional Neural Networks (CNNs) widely adopted in recent literature, and further propose an efficient ensemble network for their combination. The principles of fuzzy logic have been leveraged to combine the measured decision scores generated by three state-of-the-art CNNs - Inception V3, Inception ResNet V2 and DenseNet 201 - through the Choquet fuzzy integral. Experimental results support the efficacy of our approach over empirical ensembling, as the fuzzy ensembling strategy for biomedical measurement consists of dynamic refactoring of the classifier ensemble weights on the fly, based upon the confidence scores for coalitions of inputs. This is the chief advantage of our biomedical measurement strategy over others as other methods do not adjust to the multiple generated measurements dynamically unlike ours.Impressive results on multiple datasets demonstrate the effectiveness of the proposed method. The source code of our proposed method is made available at: https://github.com/theavicaster/covid-cade-ensemble.

This study defines a methodology to measure physical activity (PA) in ageing people working in a social garden while maintaining social distancing (SD) during COVID-19 pandemic. A real-time location system (RTLS) with embedded inertial measurement unit (IMU) sensors is used for measuring PA and SD. The position of each person is tracked to assess their SD, finding that the RTLS/IMU can measure the time in which interpersonal distance is not kept with a maximum uncertainty of 1.54 min, which compared to the 15-min. limit suggested to reduce risk of transmission at less than 1.5 m, proves the feasibility of the measurement. The data collected by the accelerometers of the IMU sensors are filtered using discrete wavelet transform and used to measure the PA in ageing people with an uncertainty-based thresholding method. PA and SD time measurements were demonstrated exploiting the experimental test in a pilot case with real users.

The importance of measurement quality cannot be over emphasized in medical applications, as one is dealing with life issues and the wellbeing of society, from oncology to new-borns, and more recently to patients of the COVID-19 pandemic. In all these dire situations, the accuracy of fluid delivered according to a prescribed dose can be critical. Microflow applications are growing in importance for a wide variety of scientific fields, namely drug development and administration, Organ-on-a-Chip, or bioanalysis, but accurate and reliable measurements are a tough challenge in micro-to-femto flow operating ranges, from 2.78 × 10 mL/s down to 2.78 × 10 mL/s (1000 μL/h down to 1 μL/h). Several sources of error have been established such as the mass measurement, the fluid evaporation dependent on the gravimetric methodology implemented, the tube adsorption and the repeatability, believed to be closely related to the operating mode of the stepper motor and drive screw pitch of a syringe pump. In addition, the difficulty in dealing with microflow applications extends to the evaluation of measurement uncertainty which will qualify the quality of measurement. This is due to the conditions entailed when measuring very small values, close to zero, of a quantity such as the flow rate which is inherently positive. Alternative methods able to handle these features were developed and implemented, and their suitability will be discussed.

Respirators are one of the most useful personal protective equipment which can effectively limit the spreading of coronavirus (COVID-19). There are a worldwide shortage of respirators, melt-blown non-woven fabrics, and respirator testing possibilities. An easy and fast filtering efficiency measurement method was developed for testing the filtering materials of respirators. It works with a laser-based particle counting method, and it can determine two types of filtering efficiencies: Particle Filtering Efficiency (PFE) at given particle sizes and Concentration Filtering Efficiency (CFE) in the case of different aerosols. The measurement method was validated with different aerosol concentrations and with etalon respirators. Considerable advantages of our measurement method are simplicity, availability, and the relatively low price compared to the flame-photometer based methods. The ability of the measurement method was tested on ten different types of Chinese KN95 respirators. The quality of these respirators differs much, only two from ten reached 95% filtering efficiency.

Despite having a small footprint origin, COVID-19 has expanded its clutches to being a global pandemic with severe consequences threatening the survival of the human species. Despite international communities closing their corridors to reduce the exponential spread of the coronavirus. The need to study the patterns of transmission and spread gains utmost importance at the grass-root level of the social structure. To determine the impact of lockdown and social distancing in Tamilnadu through epidemiological models in forecasting the "effective reproductive number" (R) determining the significance in transmission rate in Tamilnadu after first Covid19 case confirmation on March 07, 2020. Utilizing web scraping techniques to extract data from different online sources to determine the probable transmission rate in Tamilnadu from the rest of the Indian states. Comparing the different epidemiological models (SIR, SIER) in forecasting and assessing the current and future spread of COVID-19. R value has a high spike in densely populated districts with the probable flattening of the curve due to lockdown and the rapid rise after the relaxation of lockdown. As of June 03, 2020, there were 25,872 confirmed cases and 208 deaths in Tamilnadu after two and a half months of lockdown with minimal exceptions. As on June 03, 2020, the information published online by the Tamilnadu state government the fatality is at 1.8% (208/11345 = 1.8%) spread with those aged (0-12) at 1437 and 13-60 at 21,899 and 60+ at 2536 the risk of symptomatic infection increases with age and comorbid conditions.

The coronavirus COVID-19 pandemic is causing a global health crisis. One of the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). In this paper, a hybrid model using deep and classical machine learning for face mask detection will be presented. The proposed model consists of two components. The first component is designed for feature extraction using Resnet50. While the second component is designed for the classification process of face masks using decision trees, Support Vector Machine (SVM), and ensemble algorithm. Three face masked datasets have been selected for investigation. The Three datasets are the Real-World Masked Face Dataset (RMFD), the Simulated Masked Face Dataset (SMFD), and the Labeled Faces in the Wild (LFW). The SVM classifier achieved 99.64% testing accuracy in RMFD. In SMFD, it achieved 99.49%, while in LFW, it achieved 100% testing accuracy.

Metrology remains a uniquely important endeavour. A sign of its success and robustness as an infra-technology is that it usually goes unnoticed. This means that it is in danger of being under-valued and under-appreciated. The sure-footing that metrology provides to the quality infrastructure will be especially important as the world grapples with the aftereffects of the COVID-19 pandemic, rebuilding global economies and also re-focusing on addressing global grand challenges and exploiting emerging technologies. In this context it is important and timely to re-examine the concept of metrology and how it relates to the quality infrastructure that it serves, but differs to measurement in general. The concept of metrology as 'measuring measurement' is proposed, emphasising the characteristic meta-thought associated with the discipline that distinguishes it from routine measurement.

The highly pathogenic MERS-CoV, SARS-CoV and SARS-CoV-2 cause acute respiratory syndrome and are often fatal. These new viruses pose major problems to global health in general and primarily to infection control and public health services. Accurate and selective assessment of MERS-CoV, SARS-CoV and SARS-CoV-2 would assist in the effective diagnosis of infected individual, offer clinical guidance and aid in assessing clinical outcomes. In this mini-review, we review the literature on various aspects, including the history and diversity of SARS-CoV-2, SARS-CoV and MERS-CoV, their detection methods in effective clinical diagnosis, clinical assessment of COVID-19, safety guidelines recommended by World Health Organization and legal regulations. This review article also deals with existing challenges and difficulties in the clinical diagnosis of SARS-CoV-2. Developing alternative diagnostic platforms by spotting the shortcomings of the existing point-of-care diagnostic devices would be useful in preventing future outbreaks.

This paper presents a compression study of electrocardiogram (ECG) signals for e-Health cardiac online diagnostic systems. The study uses 75 real electrocardiogram records sampled with continuous-time level-crossing (LC) analog-to-digital converter (ADC). This signal-dependent LC-ADC compresses signals compared to conventional ADC but further compression is needed especially for long-time monitoring applications. The orthogonal matching pursuit algorithm is simulated to evaluate ECG compression with 54 orthogonal and biorthogonal wavelets. For LC-ADC amplitude output compression, Biorthogonal3.1 (bior3.1) wavelet achieves optimal performances in terms of compression ratio (CR) while ensuring 2-% percentage root-mean-square difference (PRD). The PRD must be limited to this value to ensure a very good quality signals after decompression. For circuit implementation purposes, bior3.1 wavelet is proposed as a multiplier-free decomposition step and a noncomplex global and hard thresholding process is achieved. The average CR is 63% and PRD varies between 0.1 and 2.1% leading to a very good diagnostic quality.

Sensor technology plays an important role in modern information and intelligence. The accuracy of sensor measurement becomes more challenging in complex working environment. In this paper, we studied relationship between output frequency difference data and corresponding loading pressure in SAW (Surface Acoustic Wave) micro-pressure sensor. Then using frequency difference as input and pressure as output, we construct BP (Back Propagation) neural network which is trained using experimental data and used to predict output pressure of the sensor. We also calculate error with actual loading pressure, same in the least squares method commonly used. Through multiple comparisons of same set of sample data in overall and local accuracy of predicted results, we verified that the output error predicted by BP neural network is much smaller than least squares method. For example, one set of data is only about 2.9%. It provided a new method for data analysis in SAW micro-pressure sensor.

The paper presents a feasibility study for heart rate measurement using a digital camera to perform health monitoring. The feasibility study investigates the reliability of the state of the art heart rate measuring methods in realistic situations. Therefore, an experiment was designed and carried out on 45 subjects to investigate the effects caused by illumination, motion, skin tone, and distance variance. The experiment was conducted for two main scenarios; human-computer interaction scenario and health monitoring scenario. The human-computer scenario investigated the effects caused by illumination variance, motion variance, and skin tone variance. The health monitoring scenario investigates the feasibility of health monitoring at public spaces (i.e. airports, subways, malls). Five state of the art heart rate measuring methods were re-implemented and tested with the feasibility study database. The results were compared with ground truth to estimate the heart rate measurement error. The heart rate measurement error was analyzed using mean error, standard deviation; root means square error and Pearson correlation coefficient. The findings of this experiment inferred promising results for health monitoring of subjects standing at a distance of 500 cm.

International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) 80000, the International System of Quantities, collects and organizes the most important physical quantities into a coherent system of quantities whose foundation for measurements is the International System of Units (SI). This short communication outlines a report that, in a similar fashion, collects and organizes the most important quantities used in measurements performed on software artifacts, focusing on software as a product rather than its development process.