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Author: 楊煜傑
Thesis Title: 基於人體狀態及交通運輸模式識別技術在智慧型手機上發展減重偵測系統
Developing a Weight Loss Monitoring System on Smartphone Using Human Behavior and Transportation Detection Technology for Healthcare
Advisor: 陳志銘
Chen, Chih-Ming
洪欽銘
Hong, Chin-Ming
Degree: 碩士
Master
Department: 工業教育學系
Department of Industrial Education
Thesis Publication Year: 2010
Academic Year: 98
Language: 中文
Number of pages: 45
Keywords (in Chinese): 智慧型手機減重偵測系統加速度感測器人體活動狀態識別交通運輸模式識別健康照護
Keywords (in English): Smartphone, Weight loss monitoring system, Accelerometer, Human activity detection, Transportation detection, Healthcare
Thesis Type: Academic thesis/ dissertation
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  • 本研究在內建三軸加速度感測器的智慧型手機平台上成功發展具識別人體活動狀態及交通運輸模式的方法。並且基於人體狀態及交通運輸模式識別結果發展一套個人熱量紀錄器,能夠提供使用者24小時的熱量消耗監測記錄並提供簡單的統計圖表,讓使用者知道每天的活動量及其所消耗熱量,作為使用者實施減重健康照護時的有效輔助工具。
    智慧型手機平台採用HTC HERO,三軸加速度感測器資料收集則採用Accelogger,並且利用Weka發展人體狀態及交通運輸模式預測模型,在測試比較決策樹、邏輯迴歸、類神經網路、支持向量機以及貝氏分類器等五種分類演算法後,以決策樹得到最佳的分類準確率。因此本研究採用決策樹當成人體狀態及交通運輸模式分類預測工具,分別可識別出走路、慢跑、搭公車、搭捷運以及靜止狀態,且準確率達到97.1954%。發展完成之系統,在隨機抽樣使用者試用後,顯示本研究發展之系統對於應用於個人減重健康照護具有實際應用價值。

    The study presents a novel scheme, which can accurately identify human activities such as running, walking, stillness and transportation statuses such as taking bus and MRT, based on samrtphone with build-in tri-axial accelerometer. Moreover, a weight loss monitoring system with precisely calculating consuming calories was successfully developed for healthcare in daily life based on the above-mentioned technologies of identifying human activities and transportation statuses. In the study, the HTC HERO smartphone with build-in tri-axial accelerometer and Android operating system was adopted as platform to develop the proposed weight loss monitoring system. Additionally, an application program named Accelogger with Fast Fourier Transformation (FFT) was employed to sense data of human activities and transportation statuses from tri-axial accelerometer for collecting training data and performing feature selection to model a prediction model. Meanwhile, the study applied Weka, which is a data mining tool, to implement the proposed prediction model of identifying human activities and transportation statuses. After comparing five well-known pattern classification schemes in Weka, decision tree has the best performance in terms of classification accuracy rate, and the classification accuracy rate on predicting three human activities and two transportation statuses is up to 97.1954%. Therefore, the study selects decision tree as prediction model for the proposed weight loss monitoring system. Finally, the proposed weight loss monitoring system was tested by six users who have different life styles during two weeks and an interview was performed to evaluate the satisfactory degree after they used the proposed system for weight loss monitoring. The experimental results show that the proposed weight loss monitoring system is indeed helpful to users to set a weight loss plan based on their self-regulated abilities.

    謝誌 I 摘要 II Abstract III 目錄 V 圖目錄 VII 表目錄 IX 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 3 1.3 研究目的及其貢獻 4 第二章 文獻探討 5 2.1 人體活動識別 5 2.2 交通運輸模式偵測 8 2.3 體重管理及其量測指標 11 第三章 研究方法 14 3.1 研究流程 14 3.2 智慧型手機平台 17 3.3 資料收集應用程式Accelogger 19 3.4 資料收集流程與方法 21 第四章 資料分析 24 4.1 資料前處理 24 4.2 資料訓練及選擇分類器 27 第五章 發展之健康照護減重偵測系統 31 5.1 熱量計算器 31 5.2 系統介紹 32 5.3 減重偵測系統使用者試用及回饋 35 第六章 結論與未來研究方向 37 6.1 結論 37 6.2 未來研究方向 38 參考文獻 39 一、中文部分 39 二、英文部分 39 作者簡介 45

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