scholarly journals Development of Metrological Maintenance of Photometric Devices For Pulsometry

2019 ◽  
pp. 10-16
Author(s):  
V. Degtjaruk ◽  
М. Khоdаkоvskyi ◽  
М. Budnyk ◽  
V. Budnyk ◽  
М. Мudrenko ◽  
...  

Investigating pulse in different parts of the body is of great interest to doctors. The purpose is the development of metrological maintenance, calibration and certification of photometric instruments [1—3]. Photoplethysmograph is designed to record changes in optical density of a person’s body area with a beam of light reflected in the light [4—6]. Measurements are carried out non-invasively [7]. Such device registers pulse wave (PW) signals and reference ECG with computer processing, Fig. 1—2 [8—10]. A working measure (LED) was created and calibrated using an optical radiation power meter based on the substitution method [11], test bench is at Fig. 3, calibration results — in Table 1 and Fig. 4. Test bench for device calibration and an optical radiator are at Fig. 5—6, view of calibrated signal — at Fig. 7. As a result of calibration (Table 2) the dependence of the output signal on LED power supply (Fig. 8) is obtained, and the calibration dependence is shown at Fig. 9. In the test bench for SMC used standardized light filters KNS-01 at a wavelength of 630 nm (Fig. 10a). The calibration curve is calculated as the dependence of the relative coefficient of inverse light dispersion (RCILD) on PW (Fig. 10b, Table 3). The view of output signal is at Fig. 11. As a result of SMC, the limits of permissible absolute error of 2 % in the range of RCILD (15—100) % are defined.

1985 ◽  
Vol 53 (11) ◽  
pp. 1108-1110 ◽  
Author(s):  
L. E. Larson ◽  
M. E. Mickelson

2004 ◽  
Vol 34 (2) ◽  
pp. 139-146 ◽  
Author(s):  
Rimma T Kuznetsova ◽  
T N Kopylova ◽  
G V Mayer ◽  
L G Samsonova ◽  
Valerii A Svetlichnyi ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3771
Author(s):  
Alexey Kashevnik ◽  
Walaa Othman ◽  
Igor Ryabchikov ◽  
Nikolay Shilov

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.


2021 ◽  
Vol 11 (4) ◽  
pp. 1667
Author(s):  
Kerstin Klaser ◽  
Pedro Borges ◽  
Richard Shaw ◽  
Marta Ranzini ◽  
Marc Modat ◽  
...  

Synthesising computed tomography (CT) images from magnetic resonance images (MRI) plays an important role in the field of medical image analysis, both for quantification and diagnostic purposes. Convolutional neural networks (CNNs) have achieved state-of-the-art results in image-to-image translation for brain applications. However, synthesising whole-body images remains largely uncharted territory, involving many challenges, including large image size and limited field of view, complex spatial context, and anatomical differences between images acquired at different times. We propose the use of an uncertainty-aware multi-channel multi-resolution 3D cascade network specifically aiming for whole-body MR to CT synthesis. The Mean Absolute Error on the synthetic CT generated with the MultiResunc network (73.90 HU) is compared to multiple baseline CNNs like 3D U-Net (92.89 HU), HighRes3DNet (89.05 HU) and deep boosted regression (77.58 HU) and shows superior synthesis performance. We ultimately exploit the extrapolation properties of the MultiRes networks on sub-regions of the body.


2010 ◽  
Vol 7 (6) ◽  
pp. 706-717 ◽  
Author(s):  
Weimo Zhu ◽  
Miyoung Lee

Background:The purpose of this study was to investigate the validity and reliability evidences of the Omron BI pedometer, which could count steps taken even when worn at different locations on the body.Methods:Forty (20 males and 20 females) adults were recruited to walk wearing 5 sets, 1 set at a time, of 10 BI pedometers during testing, 1 each at 10 different locations. For comparison, they also wore 2 Yamax Digi-Walker SW-200 pedometers and a Dynastream AMP 331 activity monitor. The subjects walked in 3 free-living conditions: a fat sidewalk, stairs, and mixed conditions.Results:Except for a slight decrease in accuracy in the pant pocket locations, Omron BI pedometers counted steps accurately across other locations when subjects walked on the fat sidewalk, and the performance was consistent across devices and trials. When the subjects climbed up stairs, however, the absolute error % of the pant pocket locations increased significantly (P < .05) and similar or higher error rates were found in the AMP 331 and SW-200s.Conclusions:The Omron BI pedometer can accurately count steps when worn at various locations on the body in free-living conditions except for front pant pocket locations, especially when climbing stairs.


2021 ◽  
Vol 2103 (1) ◽  
pp. 012159
Author(s):  
M K Mjagkih ◽  
P A Dementev ◽  
M V Zamoryanskaya

Abstract This work is devoted to the development of a method for the quantitative comparison of the luminosity of weakly luminous samples, such as self-glowing crystals. A self-glowing crystal is an efficient scintillator, whose self-luminescence is due to the decay of a radioactive isotope introduced into the crystal matrix during its growth. Such crystals can be used as low current sources with a service life of 50 years or more. This technique takes into account the luminescence spectra of the samples under study, the spectral functions of the spectrometer and photodetector. Information on the luminescence spectra of samples can be obtained based on their cathodoluminescence spectra. Thanks to the calculations performed according to this technique, it becomes possible to estimate the optical radiation power of a self-glowing crystal, which can be converted into an electric current using a photodiode. Also, the proposed technique can be applied to assess the luminosities of any materials under the influence of radioactive radiation.


2020 ◽  
Vol 299 ◽  
pp. 241-245
Author(s):  
Mikhail V. Astahov ◽  
Irina I. Sorokina ◽  
Ekaterina V. Slavkina

The possibilities of using composite materials for the modernization and repair of structures are considered. Based on a review of the sources of literature, it was concluded that the use of glue-pin combined connections is promising. For a combined transversal connection with one fastener, static and dynamic tests were performed on an Instron 1121 test bench. The loading was carried out with a normal force, pulling the fastener out the body of a polymer composite material. The analysis of experimental data is carried out, the fatigue curve is constructed.


Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3964
Author(s):  
Dražen Brščić ◽  
Rhys Wyn Evans ◽  
Matthias Rehm ◽  
Takayuki Kanda

We studied the use of a rotating multi-layer 3D Light Detection And Ranging (LiDAR) sensor (specifically the Velodyne HDL-32E) mounted on a social robot for the estimation of features of people around the robot. While LiDARs are often used for robot self-localization and people tracking, we were interested in the possibility of using them to estimate the people’s features (states or attributes), which are important in human–robot interaction. In particular, we tested the estimation of the person’s body orientation and their gender. As collecting data in the real world and labeling them is laborious and time consuming, we also looked into other ways for obtaining data for training the estimators: using simulations, or using LiDAR data collected in the lab. We trained convolutional neural network-based estimators and tested their performance on actual LiDAR measurements of people in a public space. The results show that with a rotating 3D LiDAR a usable estimate of the body angle can indeed be achieved (mean absolute error 33.5 ° ), and that using simulated data for training the estimators is effective. For estimating gender, the results are satisfactory (accuracy above 80%) when the person is close enough; however, simulated data do not work well and training needs to be done on actual people measurements.


Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3705
Author(s):  
Thi Thi Zin ◽  
Pann Thinzar Seint ◽  
Pyke Tin ◽  
Yoichiro Horii ◽  
Ikuo Kobayashi

The Body Condition Score (BCS) for cows indicates their energy reserves, the scoring for which ranges from very thin to overweight. These measurements are especially useful during calving, as well as early lactation. Achieving a correct BCS helps avoid calving difficulties, losses and other health problems. Although BCS can be rated by experts, it is time-consuming and often inconsistent when performed by different experts. Therefore, the aim of our system is to develop a computerized system to reduce inconsistencies and to provide a time-saving solution. In our proposed system, the automatic body condition scoring system is introduced by using a 3D camera, image processing techniques and regression models. The experimental data were collected on a rotary parlor milking station on a large-scale dairy farm in Japan. The system includes an application platform for automatic image selection as a primary step, which was developed for smart monitoring of individual cows on large-scale farms. Moreover, two analytical models are proposed in two regions of interest (ROI) by extracting 3D surface roughness parameters. By applying the extracted parameters in mathematical equations, the BCS is automatically evaluated based on measurements of model accuracy, with one of the two models achieving a mean absolute percentage error (MAPE) of 3.9%, and a mean absolute error (MAE) of 0.13.


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