近年來因為空氣污染以及人們生活型態的轉變，導致罹患呼吸道疾病的病患攀升。 現今大多數的呼吸道檢測儀器都置放在醫院，並且需要由專業的醫護人員協助使用。因此我們提出了雷達系統，提供病人更為簡單並且非接觸式的檢測儀器。
在此研究中，更加入了自動偵測演算法，能夠讓使用者節省設定的時間，並且更為貼近居家照護系統。在雷達系統中，我們先利用雷達記錄呼吸過程，再透過FSLW處理，萃取出人體呼吸特徵值，最後透過人體呼吸特徵值進行實驗。
為了驗證我們的系統是具有可信度的，我們將提出的人體特徵值與現今用來檢測呼吸道疾病的儀器"肺功能機"做相關性的實驗。我們透過support vector machine以及adaptive neuro-fuzzy inference system學習演算法進行分類以及預測由肺功能機量測出的呼吸參數，進而得出與肺功能機的相關系數。
在實驗中，我們也與步態系統合作，利用兩方得到的參數進行實驗。就結果而言，合作之後的結果是對分類以及預測是有幫助的。

In recent years, the air pollution and the change of lifestyle enhance the probability of suffering lung disease. Because most of measuring instruments are expensive and placed in hospitals, which are operated with the assistance of the professional.
Measuring respiration information by radar system were able to provide the convenience for subject and save the travel time to hospital. In this study, we added the automatic position into the FPGA. It can accelerate the measurement time for the subject. First, we record the subject's process of respiration, then extracted respiratory feature by FSLW model. We can use the feature to estimate and judge the condition of the respiratory system.
Until now, pulmonary spirometer is one of the main instruments to measure respiratory indexes. We set the indexes as standard value, which are extracted by pulmonary spirometer, and set FSLW's indexes as the input conditions to classify respiratory ability and predict respiratory indexes. Where we use the support vector machine (SVM) to learn and classify respiratory ability, and use the adaptive neuro-fuzzy inference system (ANFIS) to learn and predict respiratory indexes. Through the results of the experiment, it indicated that the proposed the respiratory index extracted by radar system is reliable.
We combine the gait system to increase input conditions of SVM and ANFIS. According to the results, the cooperation with gait system has improved the classification accuracy and correlation with pulmonary spirometer.

Contents
1 Introduction 3
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Radar System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Goal and Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Integrated Respiration detection System 7
2.1 Introduction of the Integrated Respiration detection System . . . . . . . 7
2.2 Introduction of COPD and Pulmonary Spirometer . . . . . . . . . . . . . 8
2.3 Gait System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4 Introduction of the Integrated Radar System . . . . . . . . . . . . . . . . 15
2.4.1 Radar Front-end-Chip . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.2 FPGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5 Respiratory Signal Feature Extraction Algorithm . . . . . . . . . . . . . 26
3 Proposed Automatically Detection Algorithm 29
4 Clinical Experiment 35
4.1 Experiment set and flow . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.3 Classification with Support Vector Machine . . . . . . . . . . . . . . . . 40
4.4 Predication with Adaptive Neural Fuzzy Inference Systems . . . . . . . . 44
4.5 Result of Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5 Conclusion and Future Work 57
5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

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