Burn depth assessment in clinics is still inaccurate because of the lack of feasible and practical testing devices and methods. Therefore, this process often depends on subjective judgment of burn surgeons. In this study, a new unilateral magnetic resonance imaging (UMRI) sensor equipped with a 2D gradient coil system was established, and we attempted to assess burns using unilateral nuclear magnetic resonance devices. A reduced Halbach magnet was utilized to generate a magnetic field that was relatively homogeneous on a target plane with a suitable field of view for 2D spatial localization. A uniplanar gradient coil system was designed by utilizing the mainstream target field method, and a uniplanar RF (radio frequency) coil was designed by using a time-harmonic inverse method for the UMRI sensor. A 2D image of the cross sections of a simple burn model was obtained by a fast 2D pure-phase encoding imaging method. The design details of the novel single-sided MRI probe and imaging tests are also presented.
With the aim to achieve an accurate lateral distance between vehicle and lane boundaries during the road test of Lane Departure Warning and Lane Keeping Assist, this study proposes a recognition model to estimate the distance directly by training a deep neural network, called LatDisLanes. The neural network model obtains the distance using two down-face cameras without data pre-processing and post-processing. Nevertheless, the accuracy of recognition is disrupted by inclination angle, but the bias is decreased using a proposed dynamic correction model. Furthermore, as training a model requires a large number of label images, an image synthesis algorithm that is based on the Image Quilting is proposed. The experiment on test data set shows that the accuracy of LatDisLanes is 94.78% and 99.94%, respectively, if the allowable error is 0.46 cm and 2.3 cm when the vehicle runs smoothly. In addition, a bigger error can be caused when inclination angle is greater than 3°, but the error can be reduced by proposing a dynamic correction model.
Purpose
Wrist-cuff oscillometric blood pressure monitors are very popular in the portable medical device market. However, its accuracy has always been controversial. In addition to the oscillatory pressure pulse wave, the finger photoplethysmography (PPG) can provide information on blood pressure changes. A blood pressure measurement system integrating the information of pressure pulse wave and the finger PPG may improve measurement accuracy. Additionally, a neural network can synthesize the information of different types of signals and approximate the complex nonlinear relationship between inputs and outputs. The purpose of this study is to verify the hypothesis that a wrist-cuff device using a neural network for blood pressure estimation from both the oscillatory pressure pulse wave and PPG signal may improve the accuracy.
Design/methodology/approach
A PPG sensor was integrated into a wrist blood pressure monitor, so the finger PPG and the oscillatory pressure wave could be detected at the same time during the measurement. After the peak detection, curves were fitted to the data of pressure pulse amplitude and PPG pulse amplitude versus time. A genetic algorithm-back propagation neural network was constructed. Parameters of the curves were inputted into the neural network, the outputs of which were the measurement values of blood pressure. Blood pressure measurements of 145 subjects were obtained using a mercury sphygmomanometer, the developed device with the neural network algorithm and an Omron HEM-6111 blood pressure monitor for comparison.
Findings
For the systolic blood pressure (SBP), the difference between the proposed device and the mercury sphygmomanometer is 0.0062 ± 2.55 mmHg (mean ± SD) and the difference between the Omron device and the mercury sphygmomanometer is 1.13 ± 9.48 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and the proposed device was 0.28 ± 2.99 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and Omron HEM-6111 was −3.37 ± 7.53 mmHg.
Originality/value
Although the difference in the SBP error between the proposed device and Omron HEM-6111 was not remarkable, there was a significant difference between the proposed device and Omron HEM-6111 in the diastolic blood pressure error. The developed device showed an improved performance. This study was an attempt to enhance the accuracy of wrist-cuff oscillometric blood pressure monitors by using the finger PPG and the neural network. The hardware framework constructed in this study can improve the conventional wrist oscillometric sphygmomanometer and may be used for continuous measurement of blood pressure.
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