scholarly journals Wearable Impedance Pneumography

2020 ◽  
Vol 6 (3) ◽  
pp. 233-236
Author(s):  
Michael Klum ◽  
Mike Urban ◽  
Alexandru-Gabriel Pielmus ◽  
Reinhold Orglmeister
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Healthy Subjects ◽  
Respiratory Diseases ◽  
High Accuracy ◽  
Flow Estimation ◽  
Respiratory Flow ◽  
Impedance Pneumography ◽  
Respiratory Parameters ◽  
Relative Flow

AbstractRespiratory diseases are a leading cause of death worldwide. The prevalence of sleep apnea, its cardiovascular consequences, postoperative respiratory instability and severe respiratory syndromes further highlight the importance of respiratory monitoring. Typical methods, however, rely on obtrusive nasal cannulas and belts. Impedance pneumography (IP) is a promising bioimpedance application which aims to estimate respiratory parameters from the thorax impedance. Currently, IP configurations require large inter-electrode distances, diminishing its applicability in a wearable context. We propose an IP configuration with 55 mm spacing using our recently presented sensor patch. In a study including 10 healthy subjects, respiratory rate (RR) and flow are estimated in the supine, lateral and prone position. Using time-delay neural network regression, RR errors below 1 bpm, flow correlations of 0.81 and relative flow errors of 38 % with respect to a pneumotachometer reference were achieved. We conclude that high accuracy RR estimation is possible in a 55 mm IP configuration. Respiratory flow can be roughly estimated. Further research combining several biosignals for a more robust, wearable flow estimation is recommended.

Hour-Glass Neural Network Based Daily Money Flow Estimation for Automatic Teller Machines

2009 ◽  
Vol 129 (7) ◽  
pp. 1325-1330
Author(s):  
Stephen Karungaru ◽  
Takuya Akashi ◽  
Miyoko Nakano ◽  
Minoru Fukumi
Keyword(s):  
Neural Network ◽  
Flow Estimation ◽  
Money Flow ◽  
Automatic Teller Machines

Estimating Pigment Concentrations from Spectral Images Using an Encoder‐Decoder Neural Network

2020 ◽  
Vol 64 (3) ◽  
pp. 30502-1-30502-15
Author(s):  
Kensuke Fukumoto ◽  
Norimichi Tsumura ◽  
Roy Berns
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Absorption Coefficient ◽  
Spectral Data ◽  
High Accuracy ◽  
Pigment Concentration ◽  
Scattering Coefficient ◽  
A Value ◽  
Input And Output ◽  
Pigment Concentrations

Abstract A method is proposed to estimate the concentration of pigments mixed in a painting, using the encoder‐decoder model of neural networks. The model is trained to output a value that is the same as its input, and its middle output extracts a certain feature as compressed information about the input. In this instance, the input and output are spectral data of a painting. The model is trained with pigment concentration as the middle output. A dataset containing the scattering coefficient and absorption coefficient of each of 19 pigments was used. The Kubelka‐Munk theory was applied to the coefficients to obtain many patterns of synthetic spectral data, which were used for training. The proposed method was tested using spectral images of 33 paintings, which showed that the method estimates, with high accuracy, the concentrations that have a similar spectrum of the target pigments.

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

Simultaneous measurement of two major prostacyclin metabolites in urine

1990 ◽  
Vol 36 (11) ◽  
pp. 1978-1980 ◽  
Author(s):  
S Zureick ◽  
J Nadler ◽  
J Yamamoto ◽  
R Horton
Keyword(s):  
Angiotensin Ii ◽  
Blood Vessels ◽  
Simultaneous Measurement ◽  
Healthy Subjects ◽  
High Accuracy ◽  
High Concentration ◽  
Accuracy And Precision ◽  
Overall Evaluation ◽  
Excretion Rates ◽  
Apparently Healthy

Abstract We describe a combined HPLC-RIA technique to measure both major metabolites of prostacyclin (PGI2): 6-keto PGF1 alpha and 2,3-dinor-6 keto PGF1 alpha. The measurement of the former, which originates from renal blood vessels, and the latter, from systemic vessels and the liver, may provide a better overall evaluation of production than measurement of one metabolite. An aliquot of acidified urine with added 3H-labeled metabolites is adsorbed and then eluted from a C18 Bond-Elut column. The sample is then passed through an HPLC system by use of an isocratic solvent combination that separates the two metabolites from known prostaglandins. The purified metabolites are then quantified by RIA. Using a logit-log10 transform, one can measure between 12 and 250 pg of either metabolite, with high accuracy and precision (CVs of 12% for a low concentration and 7% for a high concentration). Reference values for apparently healthy subjects were, respectively, 107 (SD 45) and 171 (SD 69) ng/g creatinine for 6-keto PGF1 alpha and the dinor metabolite in men (n = 18) and 45 (SD 22) and 141 (SD 28) ng/g creatinine, respectively, in women (n = 15). Indomethacin in standard doses reduced both metabolite values by 50%. Intravenous administration of angiotensin II (5 ng/kg of body wt per minute) did not alter excretion rates, but equipressor doses of norepinephrine (0.1 microgram/kg per minute) increased the production of both metabolites (6-keto greater than dinor).

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

scholarly journals Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study

2021 ◽  
Author(s):  
Satoru Tsuiki ◽  
Takuya Nagaoka ◽  
Tatsuya Fukuda ◽  
Yuki Sakamoto ◽  
Fernanda R. Almeida ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Apnea Hypopnea Index ◽  
Cephalometric Analysis ◽  
Obstructive Sleep ◽  
Main Region

Abstract Purpose In 2-dimensional lateral cephalometric radiographs, patients with severe obstructive sleep apnea (OSA) exhibit a more crowded oropharynx in comparison with non-OSA. We tested the hypothesis that machine learning, an application of artificial intelligence (AI), could be used to detect patients with severe OSA based on 2-dimensional images. Methods A deep convolutional neural network was developed (n = 1258; 90%) and tested (n = 131; 10%) using data from 1389 (100%) lateral cephalometric radiographs obtained from individuals diagnosed with severe OSA (n = 867; apnea hypopnea index > 30 events/h sleep) or non-OSA (n = 522; apnea hypopnea index < 5 events/h sleep) at a single center for sleep disorders. Three kinds of data sets were prepared by changing the area of interest using a single image: the original image without any modification (full image), an image containing a facial profile, upper airway, and craniofacial soft/hard tissues (main region), and an image containing part of the occipital region (head only). A radiologist also performed a conventional manual cephalometric analysis of the full image for comparison. Results The sensitivity/specificity was 0.87/0.82 for full image, 0.88/0.75 for main region, 0.71/0.63 for head only, and 0.54/0.80 for the manual analysis. The area under the receiver-operating characteristic curve was the highest for main region 0.92, for full image 0.89, for head only 0.70, and for manual cephalometric analysis 0.75. Conclusions A deep convolutional neural network identified individuals with severe OSA with high accuracy. Future research on this concept using AI and images can be further encouraged when discussing triage of OSA.

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