基於密度泛函理論的人體姿勢模態識別之非監督學習方法

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姓名

黃信哲(Oscar Haung) 查詢紙本館藏

畢業系所

系統生物與生物資訊研究所

論文名稱

基於密度泛函理論的人體姿勢模態識別之非監督學習方法
(Unsupervised learning of human posture pattern recognition based on the density functional theory)

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摘要(中)

本論文提出一種建立於感測器融合(Sensor Fusion)架構的人體姿勢模態辨識之非監督學習演算法。該架構包含小型穿戴式的生醫電子偵測儀器以及相應的機械學習分析技術。架構中利用輕量化的偵測器感測人體姿勢訊號，並使用雲端服務傳輸至個人電子載具。同時研發出一種機械學習分析技術進行數據的運算與分群，以達到人體姿態的識別與資料庫的建立。該方法架構於量子力學的密度泛函理論(Density Functional Theory)：利用Hamiltonian密度以及Lagrangian密度分別估計資料點機率密度函數 I 之群數以及資料邊界。除可藉由此大幅簡化系統的計算複雜度以及提升可靠度與效率，更可同時計算資料點間相互的強度與連結性，以尋找資料群的邊界進行分群。本技術針對長期實驗可用以建立個人資料數據庫：除了準確偵測與分析個別人體姿態，亦藉由資料庫更新使得各種姿態頻率的判定更加精確。在這種長期的生理訊號的分析下可以找出各種人體可能發生的病灶，例如脊椎側彎等。短期實驗可用以意外事故肇因分析以及提前的警訊通知，如老人摔倒、幼兒翻身等。除可節省大量人力、物力資源與數據資料的佔用，同時具備更高的可信度與客觀性，乃至於爾後技術的商業化。

摘要(英)

The thesis proposes an unsupervised leaning algorithm for human posture pattern recognition constructed in the framework of Sensor Fusion. The framework included a small wearable biomedical electronic sensing device and a corresponding analytical technique of machine learning. By means of the proposed sensing device, the sensed electronic signals would be transmitted to the personal electronic carriers through the cloud service. Meanwhile, the proposed algorithm would analyze the cluster behavior so that the personal database and posture patterns would then be constructed wherein. The algorithm was based on the density functional theory in the Quantum Mechanics. Using the Hamiltonian density functional and the Lagrangian density functional, the cluster number and the corresponding boundary of each cluster of a specific data probability density function I can be estimated. The proposed algorithm can not only dramatically reduce the computational complexity and reinforce the reliability and the efficiency, but also estimate the significance and the connectivity within data points to find the boundaries of clusters for further data clustering. For long-term observations, the proposed technique can be used to construct a customized database for sensing and analyzing the personal posture patterns, and also to classify each posture frequency by updating the database. Under this scenario, the possible lesions might be specified, such as the scoliosis and so forth. For short-term observations, it can be used for analysis of accident events and alert notification, such as elderly falls, children turn around, and so forth. Therefore, the proposed technique can save unnecessary costs caused from human interventions, material resources, and the occupation of storage. It also has higher credibility and objectivity, and even for the further commercialization.

關鍵字(中)

★ 姿勢模態辨識
★ 雲端服務
★ 機械學習分析技術

關鍵字(英)

★ human posture pattern recognition
★ cloud service
★ machine learning

論文目次

目錄
頁次
中文摘要 i
英文摘要 ii
致謝 iii
目錄 v
圖目錄 vi
一、緒論 1
1-1 生理資訊感測器 1
1-2 資料處理 3
1-3 本論文之焦點與未來展望 4

二、DDFT理論發展與模擬 6
2-1 資料密度泛函理論(Data DFT，DDFT) 6
2-1-1 DDFT緣起 6
2-1-2 物理上的密度泛函 6
2-1-3 統計上的密度泛函 8
2-1-4 等效性的連結 9
2-2 與高斯混合模型的比較 11
2-3 應用實際例子進行比較 13
2-4 理論總結 16
三、研究內容與方法 18
3-1 研究方法與設計 18
3-2 兩種姿態的實驗記錄與分群 21
3-3 三種姿態的分析與分群 23
四、結果與分析 25
五、結論 28
圖目錄
頁次
圖1：各項物理量和統計量的相關等效性連結 9
圖2：兩群資料相互接近之變化 11
圖3：三個群落數據使用GMM和DDFT的差別 12
圖4：使用DDFT分群優化的實際例子(腦內MRI造影) 13
圖5：使用DDFT分群優化的另一個實際例子(腦瘤偵測) 14
圖6：使用FWHM模擬並利用蛋白質染色法改進 15
圖7：演算法流程圖 16
圖8：Brainbow系統分群結果 16
圖9：三軸重力加速度感測器外觀以及實驗配置方法與實驗流程 18
圖10：實驗配置姿態展示 19
圖11：三軸加速度感測器在三種姿態變化之時序訊號關係圖 20
圖12：三軸加速度感測器在兩種姿態變化之時序訊號關係圖 21
圖13：三軸時序加速度訊號於各二維主資訊軸映射關係圖 22
圖14：針對圖12兩種姿態的GMM與DDFT分群結果 24
圖15：針對圖11三種姿態的GMM與DDFT分群結果 24
圖16：DDFT獲得的各資料邊界分群結果映射至各二維主資訊軸的關係圖……27
圖17：將針測器放進圓桶翻轉一圈之數據投射物理空間結果 27

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指導教授

郭博昭、陳健章(Bo-Zhao Guo Jian-Zhang Chen)

審核日期

2016-7-27

推文