scholarly journals Remote Blood Oxygen Estimation From Videos Using Neural Networks

2021 ◽  
Author(s):  
Joshua Mathew ◽  
Xin Tian ◽  
Min Wu ◽  
Chau-Wai Wong
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Clinical Findings ◽  
Network Architectures ◽  
Blood Oxygen ◽  
Blood Oxygen Saturation ◽  
Estimation Scheme ◽  
Physiological Sensing ◽  
The Impact ◽  
Channel Combination

<div>Blood oxygen saturation (SpO<sub>2</sub>) is an essential indicator of respiratory functionality and is receiving increasing attention during the COVID-19 pandemic. Clinical findings show that it is possible for COVID-19 patients to have significantly low SpO<sub>2</sub> before any obvious symptoms. The prevalence of cameras has motivated researchers to investigate methods for monitoring SpO<sub>2 </sub>using videos. Most prior schemes involving smartphones are contact-based: They require a fingertip to cover the phone's camera and the nearby light source to capture re-emitted light from the illuminated tissue. In this paper, we propose the first convolutional neural network based noncontact SpO<sub>2</sub> estimation scheme using smartphone cameras. The scheme analyzes the videos of a participant's hand for physiological sensing, which is convenient and comfortable, and can protect their privacy and allow for keeping face masks on.</div><div>We design our neural network architectures inspired by the optophysiological models for SpO<sub>2</sub> measurement and demonstrate the explainability by visualizing the weights for channel combination. Our proposed models outperform the state-of-the-art model that is designed for contact-based SpO<sub>2</sub> measurement, showing the potential of our proposed method to contribute to public health. We also analyze the impact of skin type and the side of a hand on SpO<sub>2</sub> estimation performance.</div>

scholarly journals Remote Blood Oxygen Estimation From Videos Using Neural Networks

2021 ◽  
Author(s):  
Joshua Mathew ◽  
Xin Tian ◽  
Min Wu ◽  
Chau-Wai Wong
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Clinical Findings ◽  
Network Architectures ◽  
Blood Oxygen ◽  
Blood Oxygen Saturation ◽  
Estimation Scheme ◽  
Physiological Sensing ◽  
The Impact ◽  
Channel Combination

<div>Blood oxygen saturation (SpO<sub>2</sub>) is an essential indicator of respiratory functionality and is receiving increasing attention during the COVID-19 pandemic. Clinical findings show that it is possible for COVID-19 patients to have significantly low SpO<sub>2</sub> before any obvious symptoms. The prevalence of cameras has motivated researchers to investigate methods for monitoring SpO<sub>2 </sub>using videos. Most prior schemes involving smartphones are contact-based: They require a fingertip to cover the phone's camera and the nearby light source to capture re-emitted light from the illuminated tissue. In this paper, we propose the first convolutional neural network based noncontact SpO<sub>2</sub> estimation scheme using smartphone cameras. The scheme analyzes the videos of a participant's hand for physiological sensing, which is convenient and comfortable, and can protect their privacy and allow for keeping face masks on.</div><div>We design our neural network architectures inspired by the optophysiological models for SpO<sub>2</sub> measurement and demonstrate the explainability by visualizing the weights for channel combination. Our proposed models outperform the state-of-the-art model that is designed for contact-based SpO<sub>2</sub> measurement, showing the potential of our proposed method to contribute to public health. We also analyze the impact of skin type and the side of a hand on SpO<sub>2</sub> estimation performance.</div>

USE OF TELECOMMUNICATIONS TO REDUCE THE IMPACT OF THE COVID-19 PANDEMIC

2021 ◽  
Vol 2 (1) ◽  
pp. 38-43
Author(s):  
Abdullajon Komilov ◽  
◽  
Madinabonu Sultonova ◽  
Durdona Orifjonova
Keyword(s):  
Oxygen Saturation ◽  
Chronic Diseases ◽  
Direct Contact ◽  
Healthcare Professionals ◽  
Automatic Monitoring ◽  
Saturation Level ◽  
Blood Oxygen ◽  
Blood Oxygen Saturation ◽  
Oxygen Saturation Level ◽  
The Impact

Today, the COVID-19 pandemic is one of the most pressing problems facing humanity. Therefore, reducing the population's level of infection with this virus is one of our government's main tasks.Therefore, it is necessary to isolate patients with chronic diseases as much as possible. Clients are more likely to be infected with caronavirus due to their age and the presence of underlying medical conditions.Limiting direct contact between such patients and observing healthcare professionals significantly reduces the patient's chances of contracting caronavirus.The article proposes a device design that allows for remote automatic monitoring of patients' condition being treated at home by an observing medical professional.With the proposed device's help, the patient's blood oxygen saturation level is automatically and remotely controlled. The device is built on an Arduino board.The use of the device greatly reduces the likelihood of contracting caronavirus in patients receiving home treatment.This could be one of the most important decisions in maintaining the health of the population today.

Use of Pulse Oximetry in Radiotherapy

2015 ◽  
Vol 1084 ◽  
pp. 515-518
Author(s):  
Nina I. Martemyanova ◽  
Natalia D. Turgunova ◽  
Aleksandr N. Aleinik
Keyword(s):  
Radiation Therapy ◽  
Pulse Oximeter ◽  
High Reliability ◽  
The Body ◽  
Good Resolution ◽  
Blood Oxygen ◽  
Blood Oxygen Saturation ◽  
Arterial Blood ◽  
Arterial Blood Oxygen ◽  
The Impact

Reflectance pulse oximeter is designed to determine arterial blood oxygen saturation during a radiation therapy. Proposed solutions promote to reduce the impact of sensor motion on the readings. Experimentally obtained optimal contact pressure of the sensor on the body is 0.7 N. The preliminary results show that the device has good resolution and high reliability.

scholarly journals An Analysis of State-of-the-art Activation Functions For Supervised Deep Neural Network

2021 ◽  
Author(s):  
Anh Nguyen ◽  
Khoa Pham ◽  
Dat Ngo ◽  
Thanh Ngo ◽  
Lam Pham
Keyword(s):  
Neural Network ◽  
Supervised Classification ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Network Architectures ◽  
Activation Functions ◽  
Scene Classification ◽  
Learning Network ◽  
Deep Learning Network

This paper provides an analysis of state-of-the-art activation functions with respect to supervised classification of deep neural network. These activation functions comprise of Rectified Linear Units (ReLU), Exponential Linear Unit (ELU), Scaled Exponential Linear Unit (SELU), Gaussian Error Linear Unit (GELU), and the Inverse Square Root Linear Unit (ISRLU). To evaluate, experiments over two deep learning network architectures integrating these activation functions are conducted. The first model, basing on Multilayer Perceptron (MLP), is evaluated with MNIST dataset to perform these activation functions.Meanwhile, the second model, likely VGGish-based architecture, is applied for Acoustic Scene Classification (ASC) Task 1A in DCASE 2018 challenge, thus evaluate whether these activation functions work well in different datasets as well as different network architectures.

scholarly journals DANNP: an efficient artificial neural network pruning tool

2017 ◽  
Vol 3 ◽  
pp. e137 ◽  
Author(s):  
Mona Alshahrani ◽  
Othman Soufan ◽  
Arturo Magana-Mora ◽  
Vladimir B. Bajic
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Model Performance ◽  
Training Data ◽  
Classification Problems ◽  
Link Type ◽  
On Line ◽  
Pruning Algorithms ◽  
Artificial Neural ◽  
The Impact

Background Artificial neural networks (ANNs) are a robust class of machine learning models and are a frequent choice for solving classification problems. However, determining the structure of the ANNs is not trivial as a large number of weights (connection links) may lead to overfitting the training data. Although several ANN pruning algorithms have been proposed for the simplification of ANNs, these algorithms are not able to efficiently cope with intricate ANN structures required for complex classification problems. Methods We developed DANNP, a web-based tool, that implements parallelized versions of several ANN pruning algorithms. The DANNP tool uses a modified version of the Fast Compressed Neural Network software implemented in C++ to considerably enhance the running time of the ANN pruning algorithms we implemented. In addition to the performance evaluation of the pruned ANNs, we systematically compared the set of features that remained in the pruned ANN with those obtained by different state-of-the-art feature selection (FS) methods. Results Although the ANN pruning algorithms are not entirely parallelizable, DANNP was able to speed up the ANN pruning up to eight times on a 32-core machine, compared to the serial implementations. To assess the impact of the ANN pruning by DANNP tool, we used 16 datasets from different domains. In eight out of the 16 datasets, DANNP significantly reduced the number of weights by 70%–99%, while maintaining a competitive or better model performance compared to the unpruned ANN. Finally, we used a naïve Bayes classifier derived with the features selected as a byproduct of the ANN pruning and demonstrated that its accuracy is comparable to those obtained by the classifiers trained with the features selected by several state-of-the-art FS methods. The FS ranking methodology proposed in this study allows the users to identify the most discriminant features of the problem at hand. To the best of our knowledge, DANNP (publicly available at www.cbrc.kaust.edu.sa/dannp) is the only available and on-line accessible tool that provides multiple parallelized ANN pruning options. Datasets and DANNP code can be obtained at www.cbrc.kaust.edu.sa/dannp/data.php and https://doi.org/10.5281/zenodo.1001086.

scholarly journals Analyzing and interpreting neural networks for NLP: A report on the first BlackboxNLP workshop

2019 ◽  
Vol 25 (4) ◽  
pp. 543-557 ◽  
Author(s):  
Afra Alishahi ◽  
Grzegorz Chrupała ◽  
Tal Linzen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Language Processing ◽  
Performance Testing ◽  
Network Architectures ◽  
Empirical Methods ◽  
Neural Models ◽  
The Impact ◽  
Systematic Manipulation

AbstractThe Empirical Methods in Natural Language Processing (EMNLP) 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category.

scholarly journals Interpolation Consistency Training for Semi-supervised Learning

2019 ◽  
Author(s):  
Vikas Verma ◽  
Alex Lamb ◽  
Juho Kannala ◽  
Yoshua Bengio ◽  
David Lopez-Paz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Supervised Learning ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Distribution ◽  
Network Architectures ◽  
Low Density ◽  
Decision Boundary ◽  
Classification Problems

We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. ICT encourages the prediction at an interpolation of unlabeled points to be consistent with the interpolation of the predictions at those points. In classification problems, ICT moves the decision boundary to low-density regions of the data distribution. Our experiments show that ICT achieves state-of-the-art performance when applied to standard neural network architectures on the CIFAR-10 and SVHN benchmark dataset.

scholarly journals Confidence-Calibrated Human Activity Recognition

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6566
Author(s):  
Debaditya Roy ◽  
Sarunas Girdzijauskas ◽  
Serghei Socolovschi
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
State Of The Art ◽  
Wearable Sensors ◽  
Window Size ◽  
Well Being ◽  
The State ◽  
Calibration Error ◽  
Network Architectures ◽  
Neural Network Architectures

Wearable sensors are widely used in activity recognition (AR) tasks with broad applicability in health and well-being, sports, geriatric care, etc. Deep learning (DL) has been at the forefront of progress in activity classification with wearable sensors. However, most state-of-the-art DL models used for AR are trained to discriminate different activity classes at high accuracy, not considering the confidence calibration of predictive output of those models. This results in probabilistic estimates that might not capture the true likelihood and is thus unreliable. In practice, it tends to produce overconfident estimates. In this paper, the problem is addressed by proposing deep time ensembles, a novel ensembling method capable of producing calibrated confidence estimates from neural network architectures. In particular, the method trains an ensemble of network models with temporal sequences extracted by varying the window size over the input time series and averaging the predictive output. The method is evaluated on four different benchmark HAR datasets and three different neural network architectures. Across all the datasets and architectures, our method shows an improvement in calibration by reducing the expected calibration error (ECE)by at least 40%, thereby providing superior likelihood estimates. In addition to providing reliable predictions our method also outperforms the state-of-the-art classification results in the WISDM, UCI HAR, and PAMAP2 datasets and performs as good as the state-of-the-art in the Skoda dataset.

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