基於影像資訊量測脈率及呼吸率之生理訊號

Abstract

使用傳統接觸式的脈率和呼吸率量測設備如心電儀需穿戴束胸帶等量測設備會造成使用者皮膚的刺激和不舒適感。因此在這研究中，開發了兩技術以非接觸式的方式以影像訊號為基礎量測脈率和呼吸率的變化。在脈率方面，因心臟跳動使得血紅蛋白流過血管，而反射光的量測可測量血紅蛋白吸收反射光的變化進而推導出脈率，所以將血紅蛋白於吸收反色光效果較強的綠色頻道抽取出來，因連續偵測下可得連續反光訊號，接著利用Ensemble Empirical Mode Decomposition的方法來獲得主要脈率頻率之訊號Intrinsic Mode Functions (IMFs)，經統計分析將各IMF透過多元線性迴歸方法(Multiple-Linear Regression)篩選出可模擬計算脈率之模型，並由各IMF之瞬時頻率透過卜瓦松結合貝式定理(Bayesian Inference for Poisson Distribution)推估平均順時頻率縮短所需之資料量達到加速計算之目的，從實驗結果以影像為基礎非接觸式的方式量測脈率可呈現此研究架構和方法與其他現行所提出的方法有明顯準確度和穩定度的提升。在呼吸率量測部分，因呼氣和吸氣過程中，可利用身體的垂直方向會呈現一個類似簡諧運動的方式來觀察呼吸變化，因此在此開發技術中首先我們先取得臉部區域，接著依對應位置獲得上半身的影像訊號，且利用Haar-like特徵找尋突出區域(Salient Region)代表上半身資訊，接著利用連續影像之垂直光流於突出區域的變化做為呼吸的輸入訊號，最後搭配過濾器和越零點方法計算出各呼吸週期。於實驗中，將討論各變因所呈現的研究數據來顯示本系統之穩定性，而變因包含呼吸時的姿勢、呼吸頻率、使用者晃動和量測距離；且與以影像為基礎之呼吸率量測之現行方法比較後，本研究所開發的技術不管是於準確度或是可容忍之變因皆優於其他開發技術，且從實驗結果可看出此研究技術已可有效的運用於真實環境中。

Contact measurements of the cardiac pulse and respiratory rate using conventional electrocardiogram (ECG) equipment requires patients to wear adhesive gel patches or chest straps that can cause skin irritation and discomfort. Therefore, two novel robust non-contact frameworks are developed for the evaluation of pulse rate and respiratory variation. In pulse rate measurement, according to the periodic variation of reflectance strength resulting from changes to hemoglobin absorptivity across the visible light spectrum as heartbeats cause changes to blood volume in the blood vessels in the face, a reflectance signal is decomposed from consecutive frames of the green channel of the facial region. Furthermore, ensemble empirical mode decomposition (EEMD) of the Hilbert-Huang Transform (HHT) is used to acquire the primary heart rate signal while reducing the effect of ambient light changes. The effective instantaneous frequencies from intrinsic mode functions decomposed by HHT are implemented by the multiple-linear regression model to evaluate heart rates before the frequencies were rectified by maximum likelihood method assuming Poisson distribution, and the minimum elapse time for heart rate evaluation is also evaluated in the estimate process. Experimental results show that our proposed approach provides a convenient non-contact method to evaluate heart rate and outperforms the current state-of-the-art method with higher accuracy and smaller variance. In respiratory rate measurement, the changes of a simple harmonic motion between inhalation and exhalation from the human’s upper body can be observed from visual appearance. Therefore, in this work to characterize the motion, a salient region is automatically selected in the energy map resulted from Haar-like features through consecutive frames. Furthermore, a median optical flow signal is used to acquire the primary respiratory rate signal. The effective respiratory frequencies resulted from vertical motion variation are decomposed by median motion signal and then characterized by zero-crossing method before the frequencies were rectified by an estimation using the noise elimination methods. In the experiment, the performance is evaluated using an extensive dataset obtained under distinct four respiratory types, regular respiration, respiration with body motion, respiration with distinct poses, and respiration with distinct capturing distances. Our approach develops a convenient non-contact method to evaluate respiratory rate with the achievement of measurement under farther distance and also outperforms the current state-of-the-art approach in terms of high correlation coefficient. Therefore, the experiment results show its efficacy for real-world environment.