臺灣博碩士論文加值系統

智能牙刷可以藉由監測刷牙方式來維持牙齒的健康，正確的刷牙位置的監測非常重要。本論文提出了一個改善刷牙位置辨識的方法。首先運用中值濾波降低感測器雜訊，並且抑制不規則的高頻刷牙運動，減少偵測的角度突波，以便得到更精確的運動特徵，接著我們使用機率神經網路(Probabilistic Neural Network, PNN)分類方法，來進行刷牙區域辨識。依據實驗結果，相對於現有k-means的刷牙區域分類方法，PNN分類方法能獲得更佳的區域分類性能，讓使用者能夠更準確監測正確刷牙狀態。此一成果可以結合智能手機作為閘道器連結雲端，藉收集即時刷牙資料，發展遠端牙齒健康照護和疾病預防的應用。

Smart toothbrushes can be designed to monitor tooth brushing procedures to facilitate the maintenance of dental health. This necessitates a monitoring mechanism that identifies proper tooth brushing regions. In this study, an improved technique for identifying tooth brushing regions was proposed. First, a median filter was used to attenuate sensor noise and control irregular high-frequency tooth brushing motions, thereby reducing angular surges during detection and obtaining as precise of tooth brushing motion characteristics as possible. Then, a probabilistic neural network (PNN) was applied to identify tooth brushing regions. The experimental results showed that, compared with the conventional k-means algorithm, the PNN was more effective at classifying tooth brushing regions to enable users to monitor their tooth brushing more precisely. The proposed technique can be integrated through smartphone gateways to cloud services to collect real-time tooth brushing data for application in the development of remote dental healthcare and dental disease prevention services.

目錄
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 論文架構 2
第二章 感測器介紹及資料處理 3
2.1 運動感測器 3
2.1.1 加速度計 3
2.1.2 磁力計 5
2.2 雜訊濾波 7
2.2.1 中值濾波 7
2.3 三維姿態估測 8
2.3.1 計算出heading角 9
2.3.2 計算roll角 10
2.3.3 計算pitch角 11
2.3.4 整合 12
2.4 機率類神經網路分類器 13
2.4.1 網路架構 13
2.4.2 網路演算法 14
第三章 刷牙區域偵測 16
3.1 特徵值 16
3.1.1 牙刷面向取得 16
3.1.2 牙刷角度取得 17
3.2 MIAT方法論 18
3.2.1 IDEF0簡介 18
3.2.2 Grafcet簡介 19
3.3 刷牙區域偵測階層式模組化系統架構 22
3.3.1 特徵擷取功能模組 22
3.3.2 區域偵測功能模組 23
3.4 刷牙區域偵測離散事件建模 24
3.4.1 特徵擷取離散事件模型 25
3.4.2 區域偵測離散事件模型 26
3.5 刷牙區域偵測軟體合成 27
3.5.1 特徵擷取軟體合成 28
3.5.2 區域偵測軟體合成 28
第四章 實驗 29
4.1 實驗環境 29
4.1.1 STM32F4 Discovery簡介 30
4.1.2 MPU9255簡介 32
4.2 分類實驗 33
4.2.1 k-means分類實驗 34
4.2.2 PNN分類實驗 35
4.3 分類性能評估 36
4.4 分析 39
第五章 結論與未來方向 41
5.1 結論 41
5.2 未來方向 41

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