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研究生(外文):Chih-Chi Huang
論文名稱(外文):A Smart Phone-based Pocket Fall Accident Prediction and Detection System
指導教授(外文):Lih-Jen Kau
外文關鍵詞:Fall PredictionFall DetectionSmart PhonePortablePocket-based
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本論文提出了一套可在智慧型行動裝置上運行的跌倒預警及偵測演算系統,透過多數手機內建且已成熟的微機電技術,直接使用手機內建的三軸加速計和陀螺儀進行訊號取得並且分析,建構出一款可攜式的口袋型跌倒偵測系統。過往的跌倒偵測相關的研究中,多著重在跌倒發生後的訊號處理或演算,換句話說人體已經撞擊到地面後系統才開始動作,而跌倒判斷也是在事發一段時間後才完成。本論文提出的演算法在於跌倒發生同時系統就同步進行訊號判斷,並且在人體撞擊地面以前即可完成跌倒判斷,不僅可進行跌倒偵測,更達到跌倒預警功能,讓傷害發生前就可以事前得知。若達到跌倒預警,救護端便可研擬對應的防護措施。欲在手機上實現跌倒預警系統,我們就必須在有限的運算資源下與時間賽跑,系統的運算複雜度和時間複雜度就不能太高。因此本論文採用支持向量機(Support Vector Machine, SVM)搭配訊號處理(濾波、量化、降取樣、二值化等)為主要系統架構來實現。本論文所提出跌倒預警系統可達91.6%的敏感度與96.6%的特異性。另一方面,本論文針對事後的跌倒偵測提出了一套精準且快速的演算法,主要採階層式判斷的概念來進行跌倒波型進行篩選,透過訊號處理技術,且搭配本論文提出之舒密特數位編碼與標準樣本訊號比較進行運算,完全不須使用頻域轉換。系統實現上不僅運算複雜度低,分別可達95%的敏感度及92.8%的特異性。
We propose in this thesis an algorithm about fall prediction and detection system which can be operated on the smart phone. Because of the advanced technology in MEMS, we can get gravity and angular velocity signal by accelerometer and gyroscope inside the smartphone and analyze the signal. We construct an portable pocket-based fall detection system. In the past, most of the research about fall detection system are focused on after occurrence processing. In other words, these systems just start to process the signal and finish after human has been impact. However, we propose an algorithm can process the signal in real time. When fall event occur, system not only start to compute the input signal but finish detection before body hit the ground. This system is a fall prediction system. We hope we can predict the fall event and prevent the injury. It must be a significant develop in home cares. If we want to achieve fall prediction system on the mobile phone, time consumption and operation count must be reduced. For this reason, support vector machine (SVM) is a good choice. Furthermore, we can use signal processing skill including filtering, quantization, down sample and binarization to support the main system. In our fall prediction system, we have 91.6% in sensitivity and 96.6% in specificity. On the other hand, we also have a fast and good accuracy algorithm for fall detection after impact to repair the low performance in fall prediction. Afterward fall detection is a hierarchy structure to determine the feature by signal process, Schmitt trigger and standard signal comparing. Without frequency transform, just low operation but get 95% in sensitivity and 92.8% in specificity.
摘要 i
誌謝 iv
目錄 v
圖目錄 vii
表目錄 ix
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 1
1.3 文獻回顧 2
1.3.1 定點式跌倒偵測 3
1.3.2 穿戴式跌倒偵測 3
1.3.3 跌倒預警 4
第二章 理論探討 5
2.1 支持向量機 5
2.1.1 最大分類邊界 5
2.1.2 線性支持向量機 7
2.2 核心函數 8
2.2.1 基本核心介紹 8
2.2.2 Kernel Trick 10
2.3 感測器介紹 11
第三章 研究方法 14
3.1 前處理 15
3.1.1 訊號校準 15
3.1.2 移動平均 16
3.1.3 觸發機制 17
3.2 跌倒預警 18
3.2.1 訊號觀察 18
3.2.2 跌倒預警支持向量機 21
3.3 跌倒偵測 24
3.3.1 訊號觀察 24
3.3.2 訊號正規化 24
3.3.3 類比數位轉換 26
3.3.4 標準樣本建立 28
3.3.5 數位關聯性決策 30
3.3.6 支持向量機分類設計 33
第四章 實驗結果 37
4.1 系統平台介紹 37
4.2 實驗環境建置 38
4.3 實驗結果 40
4.4 實驗比較 45
第五章 結論及未來展望 49
參考文獻 50
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