Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/9128
標題: 使用加強型So and Chan方法的疲勞偵測系統
Fatigue Detection System using Enhanced So and Chan Method
作者: 蘇思豪
Su, Sih-Hao
關鍵字: 心電圖
So and Chan
R波
疲勞偵測
R peak
ECG
Fatigue detection
出版社: 電機工程學系所
引用: [1] The National Highway Traffic Safety Administration (NHTSA) http://www.nhtsa.gov/ [2] 維基百科「心電圖」 http://zh.wikipedia.org/wiki/ECG [3] 維基百科「心率變異度」http://zh.wikipedia.org/wiki/HRV [4] KingNet國家網路醫院 「交感神經」與「副交感神經」 http://hospital.kingnet.com.tw/essay/essay.html?pid=18479 [5] American Heart Association, Inc - Heart Rate Variability http://circ.ahajournals.org/content/93/5/1043.full [6] 中華自律神經醫學會自律神經失調HRV檢測及治療衛教手冊 http://hrvtw.blogspot.tw/2010/09/hrvq.html [7] MIT-BIH http://physionet.org/cgi-bin/atm/ATM [8] H.H. So and K.L. Chan, “Development of QRS detection method for real-time ambulatory cardiac monitor,” in Proc. The 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Oct. 1997, vol. 1, pp. 289-292. [9] G.M. Friesen, T.C. Jannett, M.A.Jadallah, S.L. Yates, S.R. Quint, and H.T. Nagle, “A comparison of the noise sensitivity of nine QRS detection algorithms,” IEEE Transactions on Biomedical Engineering, vol. 37, no. 1, pp. 85-98, Jan. 1990. [10] 陳崇慶, “A Portable Tele-Emergent System Supporting Electrocardiogram Discrimination,” 國立台北科技大學電腦通訊與控制研究所,碩士學位論文, 2004. [11] P. Jiapu and W. J. Tompkins, “A real-time QRS detection algorithm,” IEEE Transactions on Biomedical Engineering, vol. 32, no. 3, pp. 230-236, Mar. 1985. [12] 陳學儒,“A High-Precision Real-Time Premature Ventricular Contraction (PVC) Detection System Based on Wavelet Transform,” 國立中興大學電機工程學系碩士學位論文, 2011. [13] 羅華義, “Embedded System Implementation of Drowsiness Detection Based on Frequency Domain Analysis of Heart Rate Variability,” 國立中興大學電機工程學系碩士學位論文, 2012. [14] 黃明智,“A Digital Signal Processing Based Method for Establishing the relationship Between the HRV and RDI,” 國立中山大學機械與機電工程學系碩士論文, 2006. [15] 林羣晨, “A Cardiac Health Expert System Based On Electrocardiogram (ECG) ,”慈濟大學醫學資訊研究所碩士論文, 2007. [16] 蔡慶偉, “Real-Time Heart Rate Variability Analysis with Physical Activity Detection System, ” 國立陽明大學醫學工程研究所碩士論文, 2007 [17] H. A. N. Dinh, D. K. Kumar, N.D. Pah, and P. Burton, “Wavelets for QRS detection,” in Proc. The 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Oct. 2001. vol. 2, pp. 1883-1887. [18] Y. Li and X. Chen, “A robust R-Wave detection algorithm in ECG signal,” in Proc. International Conference on Transportation, Mechanical, and Electrical Engineering, Dec. 2011, pp. 2433-2436. [19] B.G. Lee and W.Y. Chung, “Driver alertness monitoring using fusion of facial features and bio-signals,” IEEE Sensors Journal, vol. 12, no. 7, pp. 1416-2422, July 2012. [20] H.S. Shin, S.J. Jung, J.J. Kim and W.Y. Chung,“ Real time car driver’s condition monitoring system, ” in Proc. IEEE Sensors, Nov. 2010, pp. 951–954. [21] http://www.polar.fi/e_manuals/RCX3 [22] R. C.-H. Chang, C.-H. Lin, M.-F. Wei, K.-H. Lin, and S.-R. Chen, “High-precision real-time premature ventricular contraction (PVC) detection system based on Wavelet transform,” Journal of Signal Processing Systems, published online: 17 July 2013.
摘要: 現代社會交通工具愈來愈發達,路上的汽機車也愈來愈多,不論是汽車或是機車甚至是飛機都需要人為的操控,當駕駛人疲勞時會有注意力不集中、反應變慢、動作呆板、視線模糊、精神恍惚等現象,嚴重時會對車輛失去控制能力,安全的駕駛是需要高度精神集中的,若因身體疲勞而勉強繼續駕駛將可能導致不可挽回的悲劇。 一般較常見的疲勞偵測系統是用利用駕駛的點頭頻率或是眨眼睛次數和頻率做判斷,但上述的方法通常是駕駛已經進入疲勞狀態後才發出警示,無法在先前做預防和警告駕駛人,本論文使用心率變異度分析(Heart Rate Variability, HRV)在頻域上進行分析來偵測駕駛人是否進入疲勞狀態,由於心率變異是有前兆的,用這種方式作為疲勞偵測將比由外部擷取資訊來進 行判斷較能提早並警示駕駛人,有利於防止交通意外發生。 本論文的重點是提出Enhanced So and Chan Method來改善原來的R波偵測演算法(So and Chan Method),此方法可有效地降低R波的偵測錯誤,將R波偵測正確率從94.61%提升到99.16%,我們使用FPGA開發板來完成R波偵測的驗證,然後將得到的RRI資料匯入PC並進行功率頻譜密度(power spectral density, PSD)分析可得到LF/HF,其結果可用來判斷駕駛人的精神疲勞狀態。 
Nowadays, there are more and more vehicles on the road. No matter cars, scooters, even airplanes, all of them need people manipulated. When people get tired, they may feel vision blurs, and slow reaction, and can not concentrate on driving. It will make people loss of ability to control the vehicles. Safe driving requires high level of mental concentration. Fatigue driving might cause serious traffic accidents. The common fatigue detection systems use nod or blink frequency to make judgment. However, the above methods are using the information that the driver has already entered fatigue state . Hence, it can not early warn the drivers. In this thesis, we use heart rate variability analysis (Heart Rate Variability Analysis, HRVA) in the frequency domain to detect whether the driver enters fatigue state or not. Because there is a precursor of heart rate variability. we can warn the drivers earlier by this method and it’s a good help to prevent traffic accident. To improve the “So and Chan method,” an “Enhanced So and Chan Method” is proposed in this thesis. It can effectively reduce the fail detection rate. A FPGA board is used to verify our algorithm. Then we take the RRI data into PC for power spectral density analysis to get LF / HF. The results can be used to determine the driver''s state.
URI: http://hdl.handle.net/11455/9128
其他識別: U0005-2608201313045800
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2608201313045800
Appears in Collections:電機工程學系所

文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.