臺灣博碩士論文加值系統

本論文開發一套能夠在使用滑鼠時可以同時量測心率的裝置，主要的硬體架構以四組近接感測器、濾波器放大器與微控制器所完成。藉由多感測器訊號權重總和的演算法，將訊號品質較好的訊號權重調高以進行總和，最後再利用心率演算法，找出其波峰並換算成心率，回饋到電腦端給使用者。
實驗結果顯示，靜態實驗中總和訊號的靈敏度為100.00%，偵錯率為0.00%；慢速移動實驗中，總和訊號加入移動雜訊後，仍保持高靈敏度為91.88%與低偵錯率為0.75%；快速移動實驗中靈敏度與偵錯率有比較明顯的影響，但靈敏度仍維持86.67%以上，偵錯率則維持在1.89%以內；瀏覽電子論文的實驗中，靈敏度與偵錯率分別保持在92.56%與0.35%。除了快速移動的實驗，其他的實驗皆顯示本研究具有高靈敏度與低偵錯率的高準確率的特性，而快速移動的實驗仍能維持在86.67%的靈敏度與1.89%的偵錯率。
在實驗結果中，單一受試者權重總和訊號的靈敏度不一定為最高，但以整體來說，權重總和訊號的靈敏度和偵錯率與其他通道比較都為最佳或次佳，證明利用本論文提出的多感測器架構與總和權重的演算法，能夠有效的提升訊號的可用性。

The aim of the research is to develop a device to detect pulse rate while using the mouse. The main hardware consists of four proximity sensors, filters, amplifiers and a microcontroller. By mixing the signals from channels for the weighted algorithm, we select better quality signals with higher weights to sum up. Then use pulse rate algorithm to find the peaks and turning them into pulse rate, and finally transmits the calculating results to the computer for the users.
The outcome shows the sensitivity of summed signal in the rest environment is 100%, and the error detection rate is 0%. During the slow movement experiment, which the motion artifact is added to the summed signal, a high sensitivity of 91.88% and low error detection rate of 0.75% results are maintained. There is distinct influence on the rapid movement experiment, but the sensitivity is still more than 86.67%, and the error detection rate is less than 1.89%. In the experiment of browsing through electronic theses and dissertations, the sensitivity and the error detection rate is 92.56% and 0.35%, respectively. Except the rapid movement experiment, others all indicate that this research has high sensitivity and low error detection rate.
The sensitivity of the summed signal from each subject may not be the highest. Nevertheless, comparing to other channels, the sensitivity and the error detection rate of summed signal in this research are in the first or the second place, which proves that the multi-sensor and the weighted summed algorithm in this research could effectively increase the signal usability.

目錄
摘要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 VII
表目錄 X
第一章、 緒論 1
1.1 動機與目的 1
1.2 文獻回顧 4
1.3 論文架構 7
第二章、 背景與原理 8
2.1 PPG訊號 8
2.1.1 PPG量測原理 8
2.1.2 PPG的量測方法 10
2.1.3 訊號來源為手掌之血管分佈 11
2.1.4 PPG的生理參數應用 12
2.1.5 移動雜訊 12
第三章、 研究方法 13
3.1 系統架構 13
3.2 硬體感測模組 14
3.2.1 PPG感測器模組 15
3.2.2 四通道帶通濾波 16
3.2.3 微處理器 18
3.3 電腦端人機介面 19
3.3.1 電腦端藍牙接收 20
3.3.2 人機顯示介面 21
3.4 微處理器偵測心率單元 23
3.4.1 數位訊號處理 24
3.4.2 波峰偵測 26
3.4.3 訊號權重總和演算法 29
3.4.4 心率換算方法與偵測結果 30
3.5 實驗設計 31
3.5.1 參考裝置 31
3.5.2 實驗驗證 32
3.5.3 實驗設置 34
第四章、 實驗結果與討論 37
4.1 靜態實驗 37
4.1.1 實驗結果 37
4.2 慢速移動實驗 41
4.2.1 實驗結果 41
4.2.2 移動方向的討論 46
4.3 快速移動實驗 47
4.3.1 實驗結果 47
4.4 瀏覽實驗 53
4.4.1 實驗結果 53
4.5 不同部位感測器與總和訊號的討論 57
4.6 滑鼠移動與點擊的討論 57
4.7 靈敏度與偵錯率的討論 58
4.8 平均與權重演算法的討論 58
第五章、 結論 59
第六章、 未來展望 60
第七章、 參考文獻 61
附錄一、微處理器規格 66
附錄二、藍牙模組規格 67
附錄三、靜態實驗受試者各個通道比較 68
附錄四、慢速移動實驗受試者各個通道比較 71
附錄五、快速移動實驗受試者各個通道比較 77
附錄六、瀏覽實驗受試者各個通道比較 83
附錄七、心率驗證 86

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