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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.

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.

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.

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.

This paper aims to explore the relationship between the characteristics of social media health information and its adoption. The purpose is to identify information characteristics that can be used to estimate the level of health information adoption in advance.

According to the Information Adoption Model (IAM), the study extracted ten information characteristics from the aspects of information quality and information source credibility. The sample data was collected from the top ten influential health accounts based on the Impact List of Sina Weibo to test the effectiveness of these characteristics in distinguishing information at different levels of adoption. The forecasting of information adoption level is regarded as a binary classification question in the study and support vector machine (SVM) is used to do the research.

The results indicate that ten information characteristics chosen in this study are related to information adoption. Based on these information characteristics, it is feasible to estimate the level of health information adoption, and the estimation accuracy is relatively high.

A lot of work has been done in previous researches to reveal the factors that influence information adoption. The theoretical contribution of this work is to further discuss how to use the influencing factors to do some predictive work for information adoption. In practice, it will help health information publishers to disseminate high-quality health information more effectively as well as promote the adoption of health information.