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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪一平(Yi-Ping Hung) | |
| dc.contributor.author | Po-Hsiang Hsu | en |
| dc.contributor.author | 許博翔 | zh_TW |
| dc.date.accessioned | 2021-06-16T06:31:35Z | - |
| dc.date.available | 2024-12-31 | |
| dc.date.copyright | 2014-08-12 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-08-06 | |
| dc.identifier.citation | [1] Peper E., Harvey R., Lin I., Tylova H., Moss D. (2007). 'Is there more to blood volume pulse than heart rate variability, respiratory sinus arrhythmia, and cardio-respiratory synchrony?'. Biofeedback 35 (2): 54–61.
[2] M.Z. Poh, D.J. McDuff and R.W. Picard,'Non-contact, automated cardiac pulse measurements using video imaging and blind source separation,'Optics Express, vol. 18, no. 10, pp. 10762-10774,2010. [3] M.Z. Poh, D.J. McDuff, and R.W. Picard, “Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam,” IEEE Transactions on Biomedical Engineering,vol. 58, 1, pp. 7-11, january 2011. [4] S. Kwon, H. Kim, and K.S. Park, 'Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone,' Engineering in Medicine and Biology Society(EMBC), 2012 Annual International Conference of the IEEE,pp.2174-2177, 2012. [5] C.G. Scully, J. S. Lee, J. Meyer, A.M. Gorbach, D. Granquist-Fraser,Y. Mendelson, and K.H. Chon, “Physiological Parameter Monitoring from Optical Recordings With a Mobile Phone”, IEEE Transactions.,Biomedical Engineering, vol. 59, no. 2, p.303-306, 2012 [6] E. Jonathan and L. Martin, “Investigating a smartphone imaging unit for photoplethysmography,” Physiological Meas., vol. 31, no. 11, p.N79, 2010. [7] 陳昱廷, 三通道血液容積波擷取系統之研製, 碩士論文, 國立台北科技大學自動化科技研究所, 台北, 2007 [8] K. V. Madhav, M. R. Ram, E. H. Krishna, K. N. Reddy, and K. A. Reddy, 'Estimation of respiratory rate from ECG, BP and PPG signals using empirical mode decomposition,' in Proc. 28th IEEE I2MTC, Hangzhou, China, May 10–12, 2011, pp. 1611–1664. [9] J. Pan, W.J. Tompkins, 'A real-time QRS detection algorithm,' IEEE Trans. Biomed. Eng. BME-32 (3) (1985)230–236. [10] C.W. Li, C.X. Zheng, C.F. Tai, 'Detection of ECG characteristic points using wavelet transforms,' IEEE Trans. Biomed. Eng. 42(1) (1995) 21–28. [11] Y.C. Yeh, W.J. Wang 'QRS complexes detection for ECG signal: the difference operation method,' Comput. Methods Program Biomed., 91 (2008), pp. 245–254 [12] StressEraser http://stresseraser.com/ [13] Nakajima K, Tamura T and Miike H 1996 Monitoring of heart and respiratory rates by photoplethysmography using a digital filtering technique Med. Eng. Phys. 18 365–372 [14] M. C. Yu, J. L. Liou, S. W. Kuo, M. S. Lee, and Y. P. Hung. “Noncontact respiratory measurement of volume change using depth camera.” 2012 IEEE Annual International Conference of the Engineering in Medicine and Biology Society (EMBS’12 ). [15] Kinect, Microsoft, http://www.xbox.com/en-US/Kinect [16] Xtion, Asus, http://www.asus.com/Multimedia/Xtion_PRO/ [17] Norden E. Huang, et al. 'The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.' Proc. R. Soc. Lond. A(1998) 454, 903–995 [18] Nilsson L，Johansson A，Kalman S. Respiratory variations in the reflection mode photoplethysmographic signal. Relationships to peripheral venous pressure 〔J〕. Med Biol Eng Comput，2003，41:249-254. [19] pulse oximeter http://www.oximetry.org/pulseox/principles.htm [20] MIMIC database http://www.physionet.org/physiobank/database/mimicdb/ [21] R.M. Rangayyan, Biomedical Signal Analysis: A Case-study Approach, Wiley–Interscience, New York, 2001,pp. 18–28. [22] MATLAB http://www.mathworks.com/products/matlab/ [23] ALGLIB http://www.alglib.net/ [24] OpenCV http://opencv.org/ [25] Boost C++ library http://www.boost.org/ [26] MIT-BIH Database Distribution, Massachusetts Institute of Technology, Cambridge, MA, 1998. [27] ProComp infiniti 8 channel Biofeedback & Neurofeedback System v6.0 http://bio-medical.com/products/procomp-infiniti-8-channel-biofeedbackneurofeedback-system-v50.html [28] BVP Sensor http://bio-medical.com/products/heart-ratebvp-sensor.html [29] Respiration Sensor http://bio-medical.com/products/respiration-sensor.html [30] Weird Metronome http://www.weirdmetronome.com/ [31] Muench, F. (2008). The portable StressEraser heart rate variability biofeedback device: Background and research. Biofeedback, 36, 35–39. [32] Raspberry Pi http://www.raspberrypi.org/ [33] OpenGL ES http://www.khronos.org/opengles/ [34] i-connect viewX laser pico projector http://vr-zone.com/articles/i-connect-viewx-laser-pico-projector-review [35] LightStone finger sensor http://www.transparentcorp.com/products/iom/ [36] Active Shape Model code https://code.google.com/p/asmlib-opencv/ [37] FINGERTIP ChoiceMMED MD300C2 pulse oximeter http://shop.nextlevelsp.com/index.php?route=product/product&product_id=58 [38] Balakrishnan G, Durand F and Guttag J 2013 Detecting pulse from head motions in video. IEEE Conf. on Computer Vision and Pattern Recognition(Portland, OR,) pp 3430–7 [39] Li Shan, Minghui Yu 2013 “Video-based Heart rate Measurement Using Head Motion Tracking and ICA”. IEEE Conf. on International Congress on Image and Signal Processing, 6th | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/56940 | - |
| dc.description.abstract | 在這個研究中，我們提出了一個使用彩色視訊觀察使用者臉部的連續圖片在心跳時對綠色通道產生的微弱變化的即時且低花費的呼吸訊號偵測方法。因為心跳會隨著使用者的吸氣和呼氣而增加或減慢，所以呼吸訊號便可從經過一些濾波器處理的心跳間距序列獲得。此外，本研究也提出可靠度的估測並用來觀察訊號的穩定性。隨著頭部移動或光線的調整，可靠度將會立即地下降且不論在即時或事後分析都很容易觀察。
在實驗部分，我們在不同條件下測量得到的結果和呼吸感測器的訊號相似度，像是光源、背景、和攝影機的距離以及使用的通道組合。我們也在接下來的小節測量我們能達到的最高相似度而且發現可靠度越高，我們的結果和呼吸感測器訊號的相似度越高。在應用的部分，這個方法可以像一個商業化的儀器-StressEraser一樣用來引導使用者的呼吸，不僅可以消除壓力還能透過每天練習以增加心跳變異率，以及偵測使用者在睡覺時的呼吸訊號以幫助醫師觀察睡眠呼吸中止症。 這個研究的貢獻是提出一個低消費且非接觸式的即時呼吸訊號偵測方法，並且可以引導使用者達到自己的共振呼吸頻率，進而達到放鬆和增加心跳變異率的功能。 | zh_TW |
| dc.description.abstract | In this study, we propose an online low cost, non-contact respiratory signal extraction method using widely used RGB camera by measuring the subtle variations of green channel intensity from users’ face image sequence during heart pulse. Since heart rate may increase or decrease with users’ inhalation or exhalation, the respiratory signal can then be obtained from sequence of heart beats intervals after applying some filters. Besides, the evaluation of tidiness also proposed and used to observe the stability of signal. With head movement or light adjustment, tidiness will immediately decrease and easily to observe no matter in real-time or offline analysis.
In the experiment, we measure the similarity of our result and respiration sensor signal in different conditions such as light source, background, distance from webcam and combination of channels. We also measure the highest similarity we can achieve in following sections and found that our result is more similar to respiration sensor signal for subjects with higher tidiness. In the application, this method can be used to guide users’ breaths like a commercial device-StressEraser to not only relieve stress but also increase heart rate variability through daily practices and detect respiratory signal when users are sleeping, which can help doctors observe the symptom of sleep apnea. The contribution of this study is a low cost, noncontact online respiratory signal detection method which can be used to guide users to its own resonant respiratory frequency, leading them to relax and increasing heart rate variability. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T06:31:35Z (GMT). No. of bitstreams: 1 ntu-103-R01922035-1.pdf: 4481188 bytes, checksum: ab73db4de4c0077ad89f8d1bff8a9265 (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES vii LIST OF TABLES xiii Chapter 1 INTRODUCTION 1 Chapter 2 RELATED WORK 3 2.1 RGB Camera-Based Method 3 2.2 Depth Camera-Based Method 7 2.3 BVP Sensor-Based Method 8 Chapter 3 METHODOLOGY 11 3.1 Heart Beat Detection based on Color Video 11 3.1.1 Raw Data 12 3.1.2 Band Pass Filter 13 3.1.3 Difference Signal 15 3.1.4 Set Threshold 15 3.1.5 Divide & Match 16 3.1.6 Peak Alignment 17 3.1.7 Search Back 17 3.2 Respiratory Signal Extraction 18 3.3 Signal Tidiness Estimation 23 3.4 Inter-beat Interval Confidence Level Estimation 26 Chapter 4 EXPERIMENTS 28 4.1 Experimental Setup 28 4.2 Reliability Evaluation 31 4.2.1 Setup for Reference Case 31 4.2.2 Effects of Changing Background 33 4.2.3 Effects of Changing Ambient Light 35 4.2.4 Effects of Changing Measuring Distance 39 4.2.5 Effects of Changing Color of Light 41 4.3 Similarity Evaluation for Different Subjects 44 4.4 Validation of Tidiness and Confidence Level Estimation 46 4.5 Visibility of Respiratory Sinus Arrhythmia 49 Chapter 5 APPLICATIONS 53 5.1 Stress-Release 53 5.2 Fast-Asleep 56 Chapter 6 CONCLUSIONS AND FUTURE WORK 58 REFERENCE 59 | |
| dc.language.iso | en | |
| dc.title | 基於彩色視訊之即時呼吸訊號偵測 | zh_TW |
| dc.title | Online Respiratory Signal Detection based on Color Video | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 李明穗(Ming-Sui Lee) | |
| dc.contributor.oralexamcommittee | 白法堯,丁建均,陳東杰 | |
| dc.subject.keyword | 即時,非接觸式,彩色網路攝影機,呼吸訊號,相似度,可靠度, | zh_TW |
| dc.subject.keyword | online,noncontact,webcam,respiratory signal,similarity,tidiness, | en |
| dc.relation.page | 62 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-08-06 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-103-1.pdf 目前未授權公開取用 | 4.38 MB | Adobe PDF |
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