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研究生:陳柯任
研究生(外文):Ke-ren Chen
論文名稱:非固定擺放位置之智慧型手機跌倒偵測機制
論文名稱(外文):A Fall Detection Mechanism using Smartphones Placed in Unfixed Positions
指導教授:謝尚琳謝尚琳引用關係
指導教授(外文):Shang-lin Hsieh
口試委員:謝尚琳
口試委員(外文):Shang-lin Hsieh
口試日期:2013-07-29
學位類別:碩士
校院名稱:大同大學
系所名稱:資訊工程學系(所)
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:50
中文關鍵詞:三軸加速度計智慧型手機跌倒偵測
外文關鍵詞:fall detection3 axis-accelerometersmartphones
相關次數:
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本論文提出一個使用智慧型手機來判定跌倒的偵測機制,其最大的特色是不要求手機擺放在身體上某個固定位置。本機制利用手機中內建的三軸加速度計,偵測跌倒前後的三軸變化與手機的角度變化以判斷是否發生跌倒事件。當手機放置於上衣口袋、外套口袋、褲子前後口袋,還有腰包與包包時,透過本機制能有效準確地偵測出各類跌倒,並於跌倒後發出警示聲與簡訊,來通知相關人員,包括往前跌倒、往後跌倒、右側跌倒與左側跌倒,而跑步、走路、坐下、蹲下、上樓梯和下樓梯的一般動作本機制則不會誤判為跌倒。本機制跌倒偵測的Sensitivity為91.5%,平均Specificity為97.5%,此實驗數據證明本機制的有效性
The paper presents a detection mechanism that uses a smartphone to detect fall events. The main feature of the mechanism is that it does not require the smartphones to be placed in fixed positions. The mechanism utilizes the built-in 3 axis-accelerometer in the smartphone to detect the changes of the three axes and the handset’s body before and after a fall, and hence to determine if a fall event has occurred. When the smartphone running the application is placed in pockets of the pants, pockets of shirts, pockets of jacket and bags, fanny pack, the mechanism can effectively detect real falls, including forward, backward, rightward, and leftward falls. On the other hand, non-fall activities such as running, walking, sitting down, squatting, going upstairs and downstairs, and other special movement will not be mistakenly recognized as real falls. The average sensitivity of the presented detection mechanism is 91.5%, and the average specificity is 97.5%. These results prove the effectiveness of the presented mechanism.
致謝i
Abstract ii
中文摘要iii
目錄iv
圖目錄vi
表目錄viii
第一章 概論1
1.1 研究背景與動機1
1.2 研究目標3
1.3 論文架構3
第二章 相關知識4
2.1 加速度計4
2.2 跌倒姿態分類5
2.3 手機擺放位置分類5
2.4 智慧型手機跌倒偵測機制之相關研究9
第三章 非固定擺放位置之智慧型手機跌倒 偵測機制10
3.1 研究方法10
3.2 系統流程16
第四章 實驗結果與分析19
4.1 實驗環境19
4.2 實驗數據與分析20
4.3 實驗資料彙整40
4.4 實驗成果42
4.5 效能比較45
第五章 結論與未來展望47
參考文獻48
[1]「中華民國2012年至2060年人口推計」報告,2013年5月20日,檢自:http://www.cepd.gov.tw/m1.aspx?sNo=0000455
[2]「WHO Global Report on Falls Prevention in Older Age」,2013年5月20日,檢自:http://www.who.int/ageing/projects/falls_prevention_older_age/en/#
[3]劉建賢,「使用加速度計和陀螺儀之跌倒偵測系統」,大同大學資訊工程所碩士論文,2010 年。
[4]蘇明雄,「基於有限狀態機之智慧型手機跌倒偵測機制」,大同大學資訊工程所碩士論文,2012 年。
[5]Jiangpeng Dai, Xiaole Bai, Zhimin Yang, Zhaohui Shen, and Dong Xuan, “PerFallD: A Pervasive Fall Detection System Using Mobile Phones,” Pervasive Computing and Communications Workshops (PERCOM Workshops), The 8th IEEE International Conference on Digital Object Identifier, 2010.
[6]G. R. Yavuz, M. E. Kocak, G. Ergun, H. O. Alemdar, H. Yalcin, O. D. Incel, and C. Ersoy, “A Smartphone Based Fall Detector with Online Location Support,” International Workshop on Sensing for App Phones (PhoneSense), 2010.
[7]Shih-Hau Fang, Yi-Chung Liang, and Kuan-Ming Chiu, “Developing a Mobile Phone-based Fall Detection System on Android Platform,” Computing, Communications and Applications Conference (ComComAp), 2012.
[8]Zhongtang Zhao, Yiqiang Chen, Junfa Liu, and Zhongtang Zhao, “Fall Detecting and Alarming Based on Mobile Phone,” 7th International Conference on Ubiquitous Intelligence &; Computing and 7th International Conference on Autonomic &; Trusted Computing (UIC/ATC), 2010.
[9]Frank Sposaro and Gary Tyson, “iFall: An Android Application for Fall Monitoring and Response,” Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009.
[10]Yabo Cao, Yujiu Yang, and WenHuang Liu, “E-FallD: A Fall Detection System Using Android-Based Smartphone,” The 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2012.
[11]Carlo Tacconi, Sabato Mellone, and Lorenzo Chiari, “Smartphone-Based Applications for Investigating Falls and Mobility,” The 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2011.
[12]Vo Quang Viet, Gueesang Lee, and Deokjai Choi, “Fall Detection based on Movement and Smart Phone Technology,” IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2012.
[13]Shih-Hau Fang, Yi-Chung Liang, and Kuan-Ming Chiu, “Developing a Mobile Phone-based Fall Detection System on Android Platform,” Computing, Communications and Applications Conference (ComComAp), 2012.
[14]T. Tamura, T. Yoshimura, M. Sekine, M. Uchida, and O. Tanaka, “A Wearable Airbag to Prevent Fall Injuries,” IEEE Transactions on Information Technology in Biomedicine, Vol. 13, Issue 6, pp. 910-914, 2009.
[15]S. Guangyi, C. Cheung Shing, L. Wen Jung, Kwok-Sui Leung, Yuexian Zou, and Yufeng Jin, “Mobile Human Airbag System for Fall Protection Using MEMS Sensors and Embedded SVM Classifier,” IEEE Sensors Journal, Vol. 9, No. 5, pp. 495-503, 2009.
[16]BMA150:3-Axis Acceleration Sensor,2013年5月20日,檢自:http://ae-bst.resource.bosch.com/media/products/dokumente/bma150/bst-bma150-ds000-07.pdf
[17]陳慶餘、陳晶瑩,「老人跌倒之評估與處理」,台灣老年學暨老年醫學會,49期會訊文章
[18]手機放置位置之情境調查報告,2013年5月20日,檢自:http://www.mysurvey.tw/quizresult.htm?id=bbc9cf89-4659-4360-89e6-3b7d5b5255869
[19]華人健康網新聞報導,2013年7月12日,檢自:http://www.top1health.com/Article/10917
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