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系統識別號 U0026-0208201302210400
論文名稱(中文) 以可穿戴式足部感測裝置實現動作感知跌倒偵測系統
論文名稱(英文) A Motion-Aware Fall Detection System Using Pedestrian Foot Wear Sensor Devices
校院名稱 成功大學
系所名稱(中) 工程科學系碩博士班
系所名稱(英) Department of Engineering Science
學年度 101
學期 2
出版年 102
研究生(中文) 裴鴻達
研究生(英文) Hung-Ta Pei
學號 N96004206
學位類別 碩士
語文別 中文
論文頁數 85頁
口試委員 指導教授-黃悅民
口試委員-林志敏
口試委員-賴槿峰
口試委員-黃宗傳
口試委員-李維聰
中文關鍵字 跌倒偵測  三軸加速器  動作推論  步伐分析 
英文關鍵字 Fall detection  triple-axis accelerometer  motion assessment  step analysis 
學科別分類
中文摘要 跌倒事件在事故傷害類型中佔有很高的比例,其中除了老人族群容易跌倒之外,職場中之跌倒災害也占了多數,可見跌倒偵測不僅在老人族群需要,在職場安全上也逐漸受到重視。本研究提出並實作一動作感知跌倒偵測系統,使用Arduino開發版,搭配三軸加速器放置於足部,分析人類步伐之特性來當作特徵,利用藍牙無線模組傳送至手持式裝置中,將特徵輸入支持向量機以得到步伐狀態,之後再輸入至步伐分析機制求得步伐動作,接著利用學習人類平時足部步伐資訊之隱藏式馬可夫模型來推論出是否發生跌倒事件。與之前研究較不同為能將以往被視為雜訊之足部訊號分析為步伐動作,作為跌倒偵測之依據,且擺脫以往感測器綁全身影響日常行動之系統,不會影響正常生活機能,外出室內皆可配戴。本系統經過實測各種跌倒動作,其總準確率可達96%,能達到跌倒警示之目的。
英文摘要 Fall events cover a large amount of accidental occurrences. These events usually happen to elderly victims and they are also common in occupational hazards. This proves that fall detection is not only required in the elderly population but also in safety of the working public. This research proposes a motion aware fall detection system that uses the Arduino development board with the triple-axis accelerometer placed on the foot area. The device will use different characteristics of the step motion in humans as features to transfer into handheld devices via Bluetooth module. By learning the usual step motion information of users with the Hidden Markov Model to assess whether or not a fall event has occurred. The main difference of this research from previous ones is that normally the part of the feet signal analysis which is seen as noise can be translated to step motion that can be used as the proof of fall detection. The system also uses a simplified set up without constricting the user with multiple sensors which may affect the quality of the user lifestyle to allow ease of usage in indoor and outdoor environments. After actual testing of the system in different types of fall events, the system shows accuracy up to 96% to offer fall detection warning function.
論文目次 摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VII
表目錄 X
第1章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 章節提要 2
第2章 背景介紹與文獻回顧 3
2.1 非固定式跌倒偵測法 4
2.1.1 閥值分析法及相關之研究 4
2.1.2 智能演算法及相關之研究 5
2.2 感測器配戴位置 7
2.3 支持向量機 9
2.3.1 SVM基本概念 9
2.3.2 SVM線性分割 10
2.3.3 SVM線性不可分割 12
2.3.4 SVM多重分割 13
2.4 隱藏式馬可夫模型 14
2.4.1 馬可夫鏈 14
2.4.2 隱藏式馬可夫模型 15
第3章 軟硬體平台介紹與設計 18
3.1 三軸加速感測器介紹 18
3.2 微控制器 21
3.3 藍牙模組 23
3.4 智慧型裝置介紹 25
3.4.1 Android 25
3.4.2 Android應用程式生命週期 26
3.5 無線感測裝置設計 28
3.5.1 無線感測裝置 28
3.5.2 手機應用程式 29
第4章 系統原理與設計架構 32
4.1 跌倒偵測系統架構 32
4.2 人體足部動作分析 34
4.3 動作步伐分析及特徵提取 44
4.3.1 分析靜態足部訊號 45
4.3.2 分析走路步伐訊號 46
4.3.3 分析跑步步伐訊號 47
4.3.4 分析上樓梯步伐訊號 48
4.3.5 分析下樓梯步伐訊號 49
4.3.6 分析跳躍步伐訊號 49
4.3.7 分析特殊步伐訊號 50
4.3.8 步伐特徵驗證及比較 51
4.4 SVM分類模型 56
4.5 步伐分析機制 57
4.6 跌倒偵測之方法 61
4.6.1 定義跌倒事件 62
4.6.2 建立跌倒偵測之隱藏式馬可夫模型 62
4.6.3 跌倒偵測之方法 64
第5章 系統實作結果分析 66
5.1 實驗設備 66
5.2 SVM步伐分類 68
5.3 步伐分析機制 70
5.4 隱藏式馬可夫模型之跌倒判斷 75
5.5 系統比較與分析 79
第6章 結論與未來展望 81
6.1 結論 81
6.2 未來展望 81
參考文獻 82
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