scholarly journals Estimation of Cough Peak Flow Using Cough Sounds

2018 ◽  
Author(s):  
Yasutaka Umayahara ◽  
Zu Soh ◽  
Kiyokazu Sekikawa ◽  
Toshihiro Kawae ◽  
Akira Otsuka ◽  
...  
Keyword(s):  
Evaluation Method ◽  
Peak Flow ◽  
Absolute Error ◽  
Face Mask ◽  
Exponential Model ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Proposed Model ◽  
The Face ◽  
And Gender

Cough peak flow (CPF) is a measurement to evaluate the risk of cough dysfunction and can be measured using various devices, such as spirometers. However, complex device setup and the face mask required to be firmly attached to the mouth impose burdens on both patients and their caregivers. Therefore, this study develops a novel cough strength evaluation method using cough sounds. This paper presents an exponential model to estimate CPF from the cough peak sound pressure level (CPSL). We investigated the relationship between cough sounds and cough flows and the effects of a measurement condition of cough sound, microphone type, and participant’s height and gender on CPF estimation accuracy. The results confirmed that the proposed model estimated CPF with a high accuracy. The absolute error between CPFs and estimated CPFs were significantly lower when the microphone distance from the participant’s mouth was within 30 cm than when the distance exceeded 30 cm. Analysis of the model parameters showed that the estimation accuracy was not affected by participant’s height or gender. These results indicate that the proposed model has the potential to improve the feasibility of measuring and assessing CPF.

scholarly journals Estimation of Cough Peak Flow Using Cough Sounds

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2381 ◽  
Author(s):  
Yasutaka Umayahara ◽  
Zu Soh ◽  
Kiyokazu Sekikawa ◽  
Toshihiro Kawae ◽  
Akira Otsuka ◽  
...  
Keyword(s):  
Evaluation Method ◽  
Peak Flow ◽  
Absolute Error ◽  
Face Mask ◽  
Exponential Model ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Proposed Model ◽  
The Face ◽  
And Gender

Cough peak flow (CPF) is a measurement for evaluating the risk of cough dysfunction and can be measured using various devices, such as spirometers. However, complex device setup and the face mask required to be firmly attached to the mouth impose burdens on both patients and their caregivers. Therefore, this study develops a novel cough strength evaluation method using cough sounds. This paper presents an exponential model to estimate CPF from the cough peak sound pressure level (CPSL). We investigated the relationship between cough sounds and cough flows and the effects of a measurement condition of cough sound, microphone type and participant’s height and gender on CPF estimation accuracy. The results confirmed that the proposed model estimated CPF with a high accuracy. The absolute error between CPFs and estimated CPFs were significantly lower when the microphone distance from the participant’s mouth was within 30 cm than when the distance exceeded 30 cm. Analysis of the model parameters showed that the estimation accuracy was not affected by participant’s height or gender. These results indicate that the proposed model has the potential to improve the feasibility of measuring and assessing CPF.

scholarly journals Facial Gender Analysis using Gabor-DWT Feature Extraction Method

2019 ◽  
Vol 9 (2) ◽  
pp. 4904-4907
Keyword(s):  
Gabor Filter ◽  
Real Life ◽  
Optimization Method ◽  
Gender Analysis ◽  
Illumination Variation ◽  
Feature Extraction Method ◽  
Proposed Model ◽  
The Face ◽  
Age Variation ◽  
And Gender

Facial Gender Analysis has application of specific gender entry detection, human machine interface for digital marketing, real time targeted advertisement and gender demographic analysis. The facial gender can be predicted by classification of the texture and unique edges pattern. Gabor filter can extract the edge- texture patterns on the face but has problem of high dimensionality with redundancy. For accuracy enhancement, the dimension and redundancy is needed to reduce by proposed technique as maxDWT feature optimization method. The proposed model is evaluated on real life challenging dataset of face as illumination variation, POSE, face profile, age variation and obstruction on face as hat, birthmark, moles, speckles, beard, etc. Results shows that proposed technique far better than existing state of art methods of gender prediction

scholarly journals Classical and Bayesian Inference of a Mixture of Bivariate Exponentiated Exponential Model

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Refah Alotaibi ◽  
Mervat Khalifa ◽  
Ehab M. Almetwally ◽  
Indranil Ghosh ◽  
Rezk. H.
Keyword(s):  
Exponential Model ◽  
Gaussian Copula ◽  
Estimation Methods ◽  
Model Parameters ◽  
Reliability Engineering ◽  
Bivariate Model ◽  
Conditional Moments ◽  
Bayesian Paradigm ◽  
Proposed Model ◽  
Two Parameters

Exponentiated exponential (EE) model has been used effectively in reliability, engineering, biomedical, social sciences, and other applications. In this study, we introduce a new bivariate mixture EE model with two parameters assuming two cases, independent and dependent random variables. We develop a bivariate mixture starting from two EE models assuming two cases, two independent and two dependent EE models. We study some useful statistical properties of this distribution, such as marginals and conditional distributions and product moments and conditional moments. In addition, we study a dependent case, a new mixture of the bivariate model based on EE distribution marginal with two parameters and with a bivariate Gaussian copula. Different methods of estimation for the model parameters are used both under the classical and under the Bayesian paradigm. Some simulation studies are presented to verify the performance of the estimation methods of the proposed model. To illustrate the flexibility of the proposed model, a real dataset is reanalyzed.

Modeling the Recovery from Depressive Illness by an Exponential Model with Mixed Effects

2000 ◽  
Vol 39 (01) ◽  
pp. 12-15 ◽  
Author(s):  
T. Friede ◽  
F. Miller ◽  
M. Kieser
Keyword(s):  
Exponential Model ◽  
Depressive Illness ◽  
Mixed Effects ◽  
Controlled Study ◽  
Model Parameters ◽  
Rating Score ◽  
Recovery Curves ◽  
Antidepressant Treatments ◽  
Proposed Model ◽  
Treatment Of Depression

Abstract:In clinical trials of antidepressant treatments, a depression rating score is usually measured at several points of time for each patient. We propose an approach to fit data from this type of clinical trial using an exponential mixed-effects model. Compared to previous proposals, this approach has the advantage that individual recovery curves are fitted rather than curves of means. Furthermore, no artificial fixing of model parameters is needed as in other approaches. The flexibility of the proposed model is shown for various situations. The approach is illustrated by an example from a placebo-controlled study for the treatment of depression with St. John’s Wort (Hypericum perforatum).

scholarly journals Estimating the COVID-19 Death Toll by Considering the Time-Dependent Effects of Various Pandemic Restrictions

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1628
Author(s):  
Hoang Pham
Keyword(s):  
United States ◽  
Face Mask ◽  
The United States ◽  
Time Dependent ◽  
Social Distancing ◽  
Death Toll ◽  
Johns Hopkins University ◽  
Proposed Model ◽  
The World ◽  
The Face

COVID-19, known as Coronavirus disease 2019, is caused by a coronavirus called SARS-CoV-2. As coronavirus restrictions ease and cause changes to social and business activities around the world, and in the United States in particular, including social distancing, reopening states, reopening schools, and the face mask mandates, COVID-19 outbreaks are on the rise in many states across the United States and several other countries around the world. The United States recorded more than 1.9 million new infections in July, which is nearly 36 percent of the more than 5.4 million cases reported nationwide since the pandemic began, including more than 170,000 deaths from the disease, according to data from Johns Hopkins University as of 16 August 2020. In April 2020, the author of this paper presented a model to estimate the number of deaths related to COVID-19, which assumed that there would be no significant change in the COVID-19 restrictions and guidelines in the coming days. This paper, which presents the evolved version of the previous model published in April, discusses a new explicit mathematical model that considers the time-dependent effects of various pandemic restrictions and changes related to COVID-19, such as reopening states, social distancing, reopening schools, and face mask mandates in communities, along with a set of selected indicators, including the COVID-19 recovered cases and daily new cases. We analyzed and compared the modeling results to two recent models based on several model selection criteria. The model could predict the death toll related to the COVID-19 virus in the United States and worldwide based on the data available from Worldometer. The results show the proposed model fit the data significantly better for the United States and worldwide COVID-19 data that were available on 16 August 2020. The results show very encouraging predictability that reflected the time-dependent effects of various pandemic restrictions for the proposed model. The proposed model predicted that the total number of U.S. deaths could reach 208,375 by 1 October 2020, with a possible range of approximately 199,265 to 217,480 deaths based on data available on 16 August 2020. The model also projected that the death toll could reach 233,840 by 1 November 2020, with a possible range of 220,170 to 247,500 American deaths. The modeling result could serve as a baseline to help decision-makers to create a scientific framework to quantify their guidelines related to COVID-19 affairs. The model predicted that the death toll worldwide related to COVID-19 virus could reach 977,625 by 1 October 2020, with a possible range of approximately 910,820 to 1,044,430 deaths worldwide based on data available on 16 August 2020. It also predicted that the global death toll would reach nearly 1,131,000 by 1 November 2020, with a possible range of 1,030,765 to 1,231,175 deaths. The proposed model also predicted that the global death toll could reach 1.47 million deaths worldwide as a result of the SARS CoV-2 coronavirus that causes COVID-19. We plan to apply or refine the proposed model in the near future to further study the COVID-19 death tolls for India and Brazil, where the two countries currently have the second and third highest total COVID-19 cases after the United States.

scholarly journals HOW CAN MACHINE LEARNING HELP THE AUTHORITIES?

2021 ◽  
Vol 2020 (1) ◽  
pp. 370-376
Author(s):  
Samuel Ady Sanjaya ◽  
Suryo Adi Rakhmawan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Action Planning ◽  
Face Mask ◽  
Public Place ◽  
Communication Tool ◽  
Health Crisis ◽  
Surveillance Camera ◽  
Proposed Model ◽  
The Face

Corona Virus Desease (COVID-19) pandemic is causing health crisis in every region in the world, especially in Indonesia. One of the effective methods against the virus is wearing face mask in public place as the regulation made by the authorities. This paper introduces face mask detection that can be used by the authorities to make mitigation, evaluation, prevention, and action planning against COVID-19. On the other hand, this solution can be used as communication tool to evaluate people’s habit on wearing face mask. The face mask recognition in this study is developed with machine learning algorithm through the image classification method: MobileNetv2. The proposed model can be integrated with surveillance camera to impede the Covid-19 transmission by allowing the detection of people who are not wearing face mask. After the training, validation, and testing phase, the model can provide the percentage of people using face mask in some cities with high accuracy. The data produced also have a strong correlation to the vigilance index of COVID-19.

scholarly journals RESCNN: A Deep Learning Approach for Unmasking Face Mask

2021 ◽  
Vol 7 (1) ◽  
pp. 49-55
Author(s):  
Bhusra Fatima ◽  
Arun Kumar Jhapate
Keyword(s):  
Research Work ◽  
Face Mask ◽  
Surveillance Systems ◽  
The Public ◽  
Facial Region ◽  
Physical Conditions ◽  
Proposed Model ◽  
The Face ◽  
Current Scenario ◽  
Result Analysis

Due to outbreak of COVID-19 pandemic, the trend of wearing mask is rising all over the world. Before such pandemic people wear mask only to protect themselves from pollution. While other people are self-conscious about their looks, they hide their emotions from the public by hiding their faces. But in current scenario, after pandemic, it is compulsory to wear mask everywhere as researchers and doctors have proved that wearing face masks works on impeding COVID-19 transmission. Nowadays, all attendance system or surveillance systems, etc. are integrated with AI technology in which face recognition is considered as input variable. So, there is need to determine all facial landmarks to recognize an individual. In this research work, Residual Convolution Neural Network (ResCNN), network is designed and simulated which unmasks the face mask present on face and restore mask area and recognize an individual. The result analysis is performed in three different cases or scenario, one normal frontal facial region with mask, in another case the masked face is tilted and in third case the noisy masked face is taken as input. The noise in image occurs due to many physical conditions. The dataset for training of ResCNN is prepared by masking facial images taken from CelebA dataset and MFR datasets to prove the efficiency of the proposed model.

A normalized regression based regional model for generating flows at ungauged basins

2013 ◽  
Vol 68 (1) ◽  
pp. 99-108
Author(s):  
Borislava Blagojević ◽  
Jasna Plavšić
Keyword(s):  
Input Data ◽  
Absolute Error ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Efficiency Coefficient ◽  
Ungauged Basins ◽  
Model Parameter ◽  
Proposed Model ◽  
Two Parameter

Revision of existing methodologies for generating monthly-flow series at ungauged basins based on multivariate nonlinear correlation has led to a simple two-parameter model. While the existing methodology used hydrological, meteorological and geomorphologic input data, the proposed model requires hydrological and geomorphologic input data only. The proposed methodology requires formation of separate pools of donor catchments for model parameter estimates. The proposed two-parameter model and improvement in the sphere of homogeneous region identification were verified using 195 runoff data sets from hydrologic stations in Serbia in the 1961–2005 period, divided into three non-overlapping 15-year periods. Nash-Sutcliffe's model efficiency coefficient (NSE) was used to assess the: (1) quality of proposed model with identified model parameters; (2) quality of a nonlinear multivariate equation for standard normal variables estimation with identified model parameters; (3) quality of proposed model with model parameter estimates. Generated time-series statistics and nonlinear multivariate equation quality are also evaluated. Five model calibration and validation results are shown. Generated flow series variation coefficient is the best replicated statistics with relative absolute error less than 10%.

Vascular ageing and peripheral pulse: an improved model for assessing their relationship

2021 ◽  
Author(s):  
Cosimo Aliani ◽  
Eva Rossi ◽  
Piergiorgio Francia ◽  
Leonardo Bocchi
Keyword(s):  
Vascular System ◽  
Single Pulse ◽  
Exponential Model ◽  
Estimation Accuracy ◽  
Compact Representation ◽  
Model Parameters ◽  
Pulse Waveform ◽  
Fitting Procedure ◽  
Vascular Age ◽  
Improved Model

Abstract Objective:Vascular ageing is associated with several alterations, including arterial stiffness and endothelial dysfunction. Such alterations represent an independent factor in the development of cardiovascular disease. In our previous works we demonstrated the alterations occurring in the vascular system are themselves reflected in the shape of the peripheral waveform; thus, a model that describes the waveform as a sum of Gaussian curves provides a set of parameters that successfully discriminate between under(<= 35 years old) and over subjects (> 35 years old). In the present work, we explored the feasibility of a new decomposition model, based on a sum of exponential pulses, applied to the same problem. Approach: The first processing step extracts each pulsation from the input signal and removes the long-term trend using a cubic spline with nodes between consecutive pulsations. After that, a Least Squares fitting algorithm determines the set of optimal model parameters that best approximates each single pulse. The vector of model parameters gives a compact representation of the pulse waveform that constitutes the basis for the classification step. Each subject is associated to his/her "representative" pulse waveform, obtained by averaging the vector parameters corresponding to all pulses. Finally, a Bayesan classifier has been designed to discriminate the waveforms of under and over subjects, using the leave-one-subject-out validation method. Main results: Results indicate that the fitting procedure reaches a rate of 96% in under subjects and 95% in over subjects and that the Bayesan classifier is able to correctly classify 91\% of the subjects with a specificity of 94% and a sensibility of 84%. Significance: This study shows a sensible vascular age estimation accuracy with a multi-exponential model, which may help to predict cardiovascular diseases.

Methaemoglobin and Carboxyhaemoglobin Formation in Human Blood Due to NOx and CO Exposure: A Mathematical Model

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Ankit Biyani ◽  
Raj K. Vyas ◽  
Kailash Singh ◽  
Akhilendra B. Gupta ◽  
Sangeeta Vyas ◽  
...  
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Exponential Model ◽  
Invasive Technique ◽  
Model Parameters ◽  
Liquefied Petroleum Gas ◽  
Non Invasive ◽  
Proposed Model ◽  
Co Concentration ◽  
Linear Regression Technique

Combustion of Liquefied Petroleum Gas (LPG) in household kitchens results in the production of several harmful gases including Carbon Monoxide (CO) and Nitrogen Oxides (NOx). In the present work, an attempt has been made to investigate the effect of such gases on the percent oxygen saturation of blood and a nonlinear exponential model has been proposed based on reaction kinetics to quantify the amount of carboxyhaemoglobin and methaemoglobin formed in blood due to such exposures using a non-invasive technique. The model parameters have been estimated by using the experimental data obtained from exposure of the individuals to different concentrations of CO and NOx. Non-linear regression technique has been used in MATLAB® to optimise the objective function. The model has been validated for %COHb concentration in blood and corresponding data for ambient CO concentration reported in literature. The proposed model can be used to find the concentration of methaemoglobin and carboxyhaemoglobin formed in the blood for low exposures.

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