Peixi LI va publiquement soutenir sa thèse le mardi 30/03/2021 à 9h.

Le jury est composé de :

• Hélène Laurent – MCF HDR, INSA Centre Val de Loire,
• Pascal Vasseur – PU, Université de Picardie,
• Nicolas Farrugia – MCF, IMT Atlantique,
• Chao Li – CR, Académie de Sciences de Chine,
• Yannick Benezeth – MCF HDR, ImViA,
• Fan Yang Song – PU, ImViA.
   

Title : Pulse Rate Variability Measurement with Camera-based Photoplethysmography

Electrocardiogram (ECG) has been used by doctors and biomedical researchers to measure the cardiac parameters such as Heart Rate (HR) and Heart Rate VariabiLity (HRV). HR is a medical index for health monitoring and the HRV is a sign to reflect the activities of Autonomic Nervous System (ANS) and can be used for emotion detection applications. Recently, remote photoplethysmography (rPPG) has evolved as a non-contact technique for measuring vital cardiac signs. Compared with ECG, this technique is non-invasive, low-cost, comfortable and possibly utilized in long-term monitoring. It has great potential in remote health assessment and emotion detection. However, the rPPG is a video-based method, thus the measurement is not precise and the performance is heavily affected by the image noise,sensor noise, light variation, head movement, etc. Therefore, this method should be carefully studied and improved. In the PhD studies, we have focused on two major issues for the rPPG method. Firstly, the selection of region (ROi) of interest is a critical step of the technique to obtain reliable pulse signals. It should contain as many skin pixels as possible with a minimum of non-skin pixels. Secondly, as a possible replacement of HRV in some conditions, the Pulse Rate Variability (PRV) is more complicated to measure than HR because it is then necessary to detect the peaks on the temporal rPPG signal, which is usually quite noisy and has a lower temporal resolution than the signals obtained by contact equipment. Since the PRV signal is important for various applications such as remote recognition of stress and emotion, the improvement of the PRV measurement in rPPG framework is a critical task. In the PhD thesis, we firstly introduce the scientific background for the cardiac parameter measurements and related research works. Then we describe the four contributions we have made to address these issues. The first contribution is the comparative study for several ROI segmentation methods and color channel selection methods. We have found the best combination of these methods. The second contribution is a novel method for improvement of the ROI detection method. We test the algorithm in the framework of HR measurement and show that it performs better than existing methods. The third and fourth contributions are the improvement of the remote measurement of PRV with a novel one-window and two-window methods respectively. We believe that our studies have laid the foundation for realistic practice of the rPPG method.