研究生: |
陳銘翔 Ming-Xiang - Chen |
---|---|
論文名稱: |
以深度學習和時頻訊號進行心率量測 Deep Learning on Time-Frequency Representation for Heart Rate Estimation |
指導教授: |
徐繼聖
Gee-Sern Jison Hsu |
口試委員: |
林惠勇
Huei-Yung Lin 孫民 Min Sun 鍾聖倫 Sheng-Luen Chung 王鈺強 Yu-Chiang Frank Wang |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 機械工程系 Department of Mechanical Engineering |
論文出版年: | 2017 |
畢業學年度: | 105 |
語文別: | 中文 |
論文頁數: | 80 |
中文關鍵詞: | 心率 、深度學習 、時頻 |
外文關鍵詞: | heart rate, deep learning, time-frequency |
相關次數: | 點閱:369 下載:17 |
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我們提出一個應用深度學習架構進行即時心跳量測的方法。本方法共分為4個步驟,步驟1:先以CLNF演算法偵測鏡頭前的人臉與其地標點。步驟2:由地標點框選之不同的人臉區域,擷取色彩之均值訊息,並以三種不同的前處理進行訊號萃取。步驟3:應用短時傅立葉轉換(Short-Time Fourier Transform)把前述萃取之1維訊號轉為2維之時頻圖。步驟4:把時頻圖輸入一利用心率資料庫訓練完成之VGG網路進行心率預測。本論文為少數首次應用深度學習網路進行即時心率量測之研究,與目前最新透過影像之非接觸式心率量測的方法比較,本研究所提出之方法相當具有競爭力。
We propose a deep learning approach for measuring heart rates by a RGB camera. Our approach consists of four steps. In Step 1, we detect the face in front of the camera along with its landmarks using the Conditional Local Neural Fields (CLNF). In Step 2, we process the sample means of the colors on different facial regions by three preprocessing schemes. In Step 3, the Short-Time Fourier Transform (STFT) is employed to convert the three 1D processed colors into 2D Time-Frequency Representations (TFRs). Lastly in Step 4, a VGG net trained on the TFRs extracted from a training set is exploited to estimate the heart rate of the face. Our approach can be one of the pioneering works for heart rate estimation using deep learning networks. Its performance is comparable to other state-of-the-art approaches according to the experiments on benchmark databases.
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