Abstract
With the advance of science and technology, the wearable sensing device becomes more mature. For the patients, it is a very convenient way to monitor and record physiology signals with portable mobile devices. The size of wearable sensor that we used in this thesis is about 78.3 x 30.7 x 15.4 mm^3, which is small enough for users to wear it easily. In the front-end circuits of sensor node, the analog-to-digital converter (ADC) samples the electrocardiography signal in 250 Hz, respiration signal in 28 Hz and 9-axis signal in 50 Hz. By the experiments that we designed, we observe the range of record physiological data and quantify the resolution from 24-bit to 16-bit. Also, we transform the digital data to real voltage and impedance of ECG and respiration. Therefore, the data transmission and format would be adjusted more effectively. In order to plot the physiological and 9-axis signals in real-time, we used a 2D plotting framework for Apple devices. This framework named Core Plot is highly capable and customizable of not only the numerous categories of different plot but also the delicate graphical effects. We can plot multiple signals on the mobile simultaneously in real-time with this framework. Every data points we received by the wearable sensor would be preprocessed in mobile application for noise reduction and baseline wander removal. As long as these de-noise physiological signals is collected, we could observe and evaluate these data for different situations and conditions of measurements. Besides, we also detect the features of physiological signals for the further applications. During the data recording, the wearable sensor node battery life could last more than one day and the CPU usage is only 4.5% for mobile application.