應用於智慧牙刷姿態辨識的遞迴式機率神經網路

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陳彥蓁(Yen-Chen Chen) 查詢紙本館藏

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資訊工程學系

論文名稱

應用於智慧牙刷姿態辨識的遞迴式機率神經網路
(A Recurrent Probabilistic Neural Network for Posture Recognition Applying to Smart Toothbrush)

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摘要(中)

刷牙是預防各種口腔疾病的主要方法，但刷牙全面且時間足夠才能夠真正降低牙齒疾病發生率。現有的智慧牙刷相關研究，雖然能夠以刷牙時的姿態角辨識刷牙區域，但使用者的身高及牙刷擺放位置等因素無法確定，因此若使用固定的模型辨識，將導致姿態辨識精確度及穩定性不足，還有無法監測刷牙的正確性和完整度的缺點。本論文因此提出一個遞迴機率神經網路模型DRPNN，應用於智慧牙刷姿態辨識。DRPNN由系統中已存在的PNN模型抽取出適當的個人刷牙特徵建立，模型包含記憶神經單元，具有自適應能力，利用PSO演算法迭代調整參數至模型最佳狀態，實驗結果發現本論文所提出之DRPNN辨識模型，刷牙姿態辨識率可達到98.64%，透過增加遞迴記憶單元平均準確率可達到99.08%，平均辨識率比使用CNN模型辨識高16.2%，也比使用LSTM模型辨識高21.21%。模型大小遠小於CNN與LSTM神經網路模型，能夠於低成本嵌入式系統中進行即時刷牙姿態辨識，改善現有智慧牙刷成本過高、辨識精度低、和智慧化不足等缺失。

摘要(英)

Tooth brushing is the main method to prevent various oral diseases, only if thorough and long enough tooth brushing can reduce the incidence of tooth disease. In the existing studies about smart toothbrush, the tooth brushing area can be recognized by the attitude angle during tooth brushing, but the user′s body height and toothbrush location factors are uncertain. Therefore, if a fixed model is used for recognition, the posture recognition accuracy and stability will be insufficient, and the tooth brushing correctness and integrity cannot be monitored. This paper proposes a Dynamic Recurrent Probability Neural Network (DRPNN) for smart toothbrush posture recognition. The DRPNN uses the existent Probability Neural Network model in system to extract appropriate personal tooth brushing feature establishment. The model has memory cell and adaptive capability. The parameters are tuned iteratively by using Particle Swarm Optimization algorithm to the optimum condition of model. The experimental results show that the tooth brushing posture recognition rate of the recognition model proposed by this study is 98.64%. The average accuracy rate is 99.08% after the recurrent unit is used. The average recognition rate is higher than the Convolutional Neural Networks (CNN) model by 16.2%, and higher than the long short-term memory (LSTM) model by 21.21%. The model size is much smaller than the CNN and LSTM neural network models. The real-time tooth brushing posture recognition can be implemented in low cost embedded system. The deficiencies in the existing smart toothbrush can be remedied, such as high cost, low recognition accuracy and insufficient intelligence.

關鍵字(中)

★ 智慧牙刷
★ 姿態辨識
★ 遞迴式機率神經網路

關鍵字(英)

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VII
表目錄 IX
第一章、緒論 1
1.1 研究背景 1
1.2 研究目的 4
1.3 論文架構 5
第二章、文獻回顧 6
2.1 卡爾曼濾波器 6
2.2 姿態角表示法 8
2.2.1 尤拉角 8
2.2.2 四元數 14
2.3 高效率姿態估算演算法 17
2.3.1 姿態估算 17
2.4 深度神經網路序列學習 20
2.4.1 卷積神經網路 20
2.4.2 遞迴神經網路 21
2.4.3 長短期記憶模型 22
2.5 機率神經網路 25
2.6 粒子群最佳化演算法 29

第三章、嵌入式刷牙姿態神經網路辨識系統設計 31
3.1 刷牙姿態辨識演算法設計 32
3.1.1 坐標系統 33
3.1.2 辨識特徵 34
3.1.3 模型遷移 37
3.1.4 遞迴機率神經網路 38
3.1.5 自適應機率神經網路 39
3.2 嵌入式高階系統設計方法論 39
3.2.1 IDEF0系統架構 40
3.2.2 GRAFCET 42
3.3 刷牙姿態神經網路辨識系統IDEF0 44
3.3.1 感測器資料融合模組 45
3.3.2 改良式機率神經網路辨識模組 46
3.3.3 個人化遞迴機率神經網路辨識模組 47
3.4 刷牙姿態神經網路辨識系統GRAFCET 48
3.4.1 感測器資料融合GRAFCET 50
3.4.2 改良式機率神經網路GRAFCET 52
3.4.3 個人化遞迴機率神經網路辨識GRAFCET 53
3.5 系統合成 54
第四章、實驗結果與分析 55
4.1 嵌入式軟硬體開發平台 55
4.1.1 STM32F7單晶片微控制器 55
4.1.2 慣性感測器 57
4.1.3 micro SD 卡 58
4.1.4 檔案系統 60
4.1.5 Keil開發工具 60
4.1.6 Unity 61
4.2 智慧牙刷相關資料庫建立 61
4.2.1 通用模型刷牙資料庫 62
4.2.2 區域刷牙資料庫 64
4.2.3 刷牙個人化模型資料庫 64
4.3 卡爾曼濾波實驗 65
4.4 實驗評比指標介紹 69
4.5 比較之深度神經網路 70
4.6 深度學習實驗平台 71
4.6.1 Tensorflow深度學習框架 71
4.7 CNN刷牙姿態辨識實驗 72
4.8 LSTM刷牙姿態辨識實驗 76
4.9 自適應DRPNN與DPNN刷牙姿態辨識實驗 80
4.10 刷牙完整度評估 83
4.11 實驗總結與演算法討論 87
第五章、結論與未來展望 89
5.1 結論 89
5.2 未來展望 90
參考文獻 91

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指導教授

陳慶瀚

審核日期

2018-7-3

推文