隨著科技與科學的進步，穿戴式感測裝置變得更加熱門及成熟，對於病人來說，使用可攜式的行動裝置來記錄或監測生理訊號是一種非常便利的方式。
　　我們的穿戴式感測器大小約為長78.3釐米、寬30.7釐米、高15.4釐米，對於使用者來說是可以攜帶的大小，在感測器的前端電路中，類比數位轉換器取樣心電訊號的頻率為250赫茲、呼吸訊號為28赫茲、九軸訊號為50赫茲，藉由我們所設計的實驗，我們觀察記錄下來的生理訊號資料範圍並將解析度從24位元量化至16位元，同時，我們也將其數位值換算成實際的心電電壓以及呼吸阻抗，因此，資料傳輸與格式能調整的更有效率。
　　為了能即時的繪畫生理訊號以及動態訊號，我們使用了能供蘋果公司（Apple）裝置使用的平面繪圖架構，這個名為CorePlot的架構擁有非常好的能力和可調性，它不只有許多各種不同種類的繪圖還擁有精細的圖畫效果，藉由這個架構，我們可以同時在行動裝置上畫出多種訊號。
　　穿戴式感測器所接受到的每一筆資料點都會在手機應用程式中做前處理去除雜訊和基線飄移，只要能蒐集到這些去除雜訊後的生理訊號，我們就可以觀察以及評估不同情況和條件下的測量結果，此外，為了以後更進一步地應用，我們也擷取了生理訊號的特徵。
　　在紀錄生理訊號的期間，穿戴式感測器的電池壽命能持續超過一天，且手機端的應用程式處理器使用量僅有4.5%。 

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.

1 Introduction....................................P.1
2 Related Work....................................P.5
3 Proposed Physiological Sensing System...........P.17
4 System Verification and Parameters Decision.....P.45
5 System Application..............................P.61
6 Conclusions and Future Works....................P.73

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