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  本文關(guān)鍵詞：基于FastICA算法的非接觸心率、呼吸頻率檢測　出處：《南昌大學》2015年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 人臉彩色視頻 快速獨立分量分析 非接觸式測量 心率 頻譜分析

【摘要】：近幾年來我國很多城市,尤其是北京,飽受霧霾的侵擾,霧霾對人體的呼吸系統(tǒng)產(chǎn)生了極大的危害,人們呼吸道疾病發(fā)病率逐年升高,心血管方面疾病發(fā)病率也有所增加。心率作為心血管疾病檢查的最基礎(chǔ)表征指數(shù),對人體的健康指標有重要意義。快速和方便的心率測量成為日常生活的一個方面。本課題基于視頻圖像處理,將一種快速獨立分量分析的方法(FastICA)運用到人臉圖像信號處理領(lǐng)域,并由此測量出心率。在自然環(huán)境下,通過網(wǎng)絡(luò)攝像頭采集人臉彩色視頻,從視頻流中檢測出人臉并提取心率信號,實現(xiàn)簡單的非接觸式心率測量。本課題的研究對人們便捷檢查自身心血管方面的健康,降低未知的危險,有著十分重要的意義。該方法在不接觸人體皮膚的情況下,間接檢測出人的心率。相比較傳統(tǒng)的接觸式心率檢測的方法,這種檢測方法具有無創(chuàng)、簡單、高效等特點。基于光電容積描記法(PPG)的原理,本文先綜述了基于人臉視頻的非接觸心率檢測的研究歷史、現(xiàn)狀和理論基礎(chǔ),然后將基于FastICA算法的獨立分量分析運用到觀測信號的盲源分離。該算法能對混合信號進行某種最優(yōu)線性分解并獲取統(tǒng)計獨立的分量。從三個原始的觀測信號分解得到三個獨立信號,對信號進行相關(guān)性分析、平滑、濾波、插值等得出信號周期,間接計算出心率。人的臉部圖像信息還包含了另一項生理參數(shù)——呼吸頻率。因為信號的高頻分量和呼吸頻率是有聯(lián)系的,本文對最終得到的心博周期信號采用頻譜分析,并從功率譜中估計呼吸頻率。最后,通過實驗仿真驗證,對比手指式血氧飽和儀的測量數(shù)據(jù),本文驗證了該方法的可靠性。
[Abstract]:In recent years many of our city, especially in Beijing, suffering from haze intrusion, haze on the human respiratory system brought tremendous harm to people, incidence of respiratory diseases increased year by year, the incidence of cardiovascular disease also increased. The most basic characterization index of heart rate as a cardiovascular disease examination, are important health indicators on the human body. The rapid and convenient measurement of heart rate has become one aspect of daily life. This paper based on video image processing method, a fast independent component analysis (FastICA) is applied to face image signal processing field, and thus the measured heart rate. In the natural environment, through the network camera face color video to detect the face, and the extraction rate of signal from the video stream, non-contact measurement of heart rate is simple. The research of the people convenient to check their cardiovascular health Kang, reduce the unknown risk, has very important significance. This method is not in contact with the human skin under the condition of a person's heart rate. The indirect detection method for contact detection rate compared with the traditional, this method is non-invasive, simple, efficient and so on. Based on the method of photoplethysmography (PPG the principle), this thesis first reviews the research history of non contact rate of face detection based on video, status and theoretical basis, and then the independent component analysis FastICA algorithm applied to blind source separation based on the observed signal. This algorithm can of mixed signal in some optimal linear decomposition and obtain statistically independent components from three. The original signal is decomposed into three independent observation signal, correlation analysis for signal smoothing, filtering, interpolation and so that the signal cycle, and calculate the heart rate. The human face image information also contains another student Physical parameters of respiratory frequency. Because of the high frequency component and respiratory frequency signals are connected, the cardiac cycle signal obtained by spectrum analysis, and estimate the respiratory rate from the power spectrum. Finally, through simulation experiments, comparing with the measurement data of finger type blood oxygen saturation instrument, this paper verifies the reliability of the method.

【學位授予單位】：南昌大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：R443;TP391.41

【共引文獻】

相關(guān)期刊論文 前1條

1 尹洪偉;李國林;路翠華;;一種改進的雙因子自適應(yīng)FastICA算法[J];四川大學學報(工程科學版);2014年06期

相關(guān)碩士學位論文 前1條

1 唐興佳;加權(quán)正交約束盲信號分離算法及其收斂性研究[D];西安電子科技大學;2014年

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本文編號：1361363