Noncontact image-based vital signs detection is attracting considerable compared to contact-based approaches due to hygiene, robustness, and cost-effectiveness. It is possible to measure simultaneously multiple individuals to apply for various surveillance applications. Widely available noncontact image-based detection system helps to check the vital signs at home via a normal camera. Therefore, this dissertation aims to build a fully intelligent noninvasive biomedical signal detection from image analysis in terms of clinical scenarios. We derive from the Eulerian and Lagrangian perspectives to build a system to detect breathing rate, heart rate, and blood pressure values. The state-of-the-art object detection and instance segmentation algorithms, including Yolov3, Faster-RCNN, Deeplabv3+, etc, are regularly used to localize the interesting bounding boxes (chest, face, palm). Pyramidal Lucas-Kanade and remote photoplethysmography are two main techniques for extracting the motion signals (breath, pulse) and subtle color change induced by pulse, respectively. In addition, digital signal processing is implemented to remove undesired noises for obtaining a clean biosignal. From experiments conducted, our system can detect breathing rate, heart rate of multiple individuals in real-time at a long distance in terms of motion scenarios. The X-Ray shooting assistant system can work perfectly to detect the peak times of the inspiratory phase with the error ±1 respiration per minute (rpm). For the heart rate detection system, the proposed “Adaptive Pulsatile Plane” (APP) is quite robust and stable in fitness motion cases, dim-lighting environment and the long-distance up to 4-meter away without zooming in camera. Similar to the noninvasive blood pressure estimation system, the proposed deep learning model overcomes the dependence of the high-speed camera in previous works to satisfy two medical standards (British Hypertension Society and Association for the Advancement of Medical Instrumentations) in estimating Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) with the root mean squared error and mean absolute error for SBP/DBP are 7.942/7.912 mmHg and 6.556/6.372, respectively. The proposed approach estimates blood pressure reliably by only a normal webcam with 30 fps in a non-contact continuous manner. Thus, it can be concluded that our system can be applied to healthcare applications.

Noncontact image-based vital signs detection is attracting considerable compared to contact-based approaches due to hygiene, robustness, and cost-effectiveness. It is possible to measure simultaneously multiple individuals to apply for various surveillance applications. Widely available noncontact image-based detection system helps to check the vital signs at home via a normal camera. Therefore, this dissertation aims to build a fully intelligent noninvasive biomedical signal detection from image analysis in terms of clinical scenarios. We derive from the Eulerian and Lagrangian perspectives to build a system to detect breathing rate, heart rate, and blood pressure values. The state-of-the-art object detection and instance segmentation algorithms, including Yolov3, Faster-RCNN, Deeplabv3+, etc, are regularly used to localize the interesting bounding boxes (chest, face, palm). Pyramidal Lucas-Kanade and remote photoplethysmography are two main techniques for extracting the motion signals (breath, pulse) and subtle color change induced by pulse, respectively. In addition, digital signal processing is implemented to remove undesired noises for obtaining a clean biosignal. From experiments conducted, our system can detect breathing rate, heart rate of multiple individuals in real-time at a long distance in terms of motion scenarios. The X-Ray shooting assistant system can work perfectly to detect the peak times of the inspiratory phase with the error ±1 respiration per minute (rpm). For the heart rate detection system, the proposed “Adaptive Pulsatile Plane” (APP) is quite robust and stable in fitness motion cases, dim-lighting environment and the long-distance up to 4-meter away without zooming in camera. Similar to the noninvasive blood pressure estimation system, the proposed deep learning model overcomes the dependence of the high-speed camera in previous works to satisfy two medical standards (British Hypertension Society and Association for the Advancement of Medical Instrumentations) in estimating Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) with the root mean squared error and mean absolute error for SBP/DBP are 7.942/7.912 mmHg and 6.556/6.372, respectively. The proposed approach estimates blood pressure reliably by only a normal webcam with 30 fps in a non-contact continuous manner. Thus, it can be concluded that our system can be applied to healthcare applications.

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