| Our heart rate signal is a very important signal in the biomedical signals, and the research of heart rate signal processing becomes more and more popular at home and abroad in recent years. With the development of signal processing technology and computer processing technology, people want to be able to apply modern technology on pulse diagnosis, in order to reveal the essence and characteristics of heart rate more scientific and more objective.In recent years, with the market share of Android smart phone continues to expand, we can easy to know our heart condition and to measure the strength of sports and fitness at anytime or anywhere if we have a mobile heart rate monitoring application based on the Android operating system.Therefore, this thesis describes how to design an Android heart rate monitoring application, and focus on the image processing methods, such as sampling and the gray value calculation, wavelet denoising, the signal peak searching, and cycle calculation. This thesis particularly gives a detail study of wavelet threshold denoising algorithm in the heart rate monitoring application and evaluates the accuracy when different wavelet functions, different threshold functions and different threshold are applied in the heart rate monitoring. In addition, this thesis proposed a innovative self-adaptive peak searching algorithm to improve the accuracy.Through multiple sets of field experimental results show that the denoising algorithm used Coiflet5wavelet, hard threshold function, heuristic threshold and the self-adaptive peak searching algorithm have a higher signal-to-noise ratio. And if we choose the artificial pulse measurement as a reference, the accuracy of mobile heart rate monitoring application based on the method this thesis described is more than95%, and it improves the accuracy of5%compared to other applications in current Android market, so this topic is innovative and has a practical value. |
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