Imaging Photoplethysmography (IPPG) is an emerging technique for the extraction of vital signs of human being using video recordings. IPPG technology with its advantages like non-contact measurement, low cost and easy operation has become one research hot spot in the field of biomedicine. However, the noise disturbance caused by non-microarterial area cannot be removed because of the uneven distribution of micro-arterial, different signal strength of each region, which results in a low signal noise ratio of IPPG signals and low accuracy of heart rate.
In this paper, we propose a method of improving the signal noise ratio of camera-based IPPG signals of each sub-region of the face using a weighted average. Firstly, we obtain the region of interest (ROI) of a subject’s face based camera. Secondly, each region of interest is tracked and feature-based matched in each frame of the video. Each tracked region of face is divided into 60x60 pixel block. Thirdly, the weights of PPG signal of each sub-region are calculated, based on the signal-to-noise ratio of each sub-region. Finally, we combine the IPPG signal from all the tracked ROI using weighted average. Compared with the existing approaches, the result shows that the proposed method takes modest but significant effects on improvement of signal noise ratio of camera-based PPG estimated and accuracy of heart rate measurement.
The rise of aging population has created a demand for inexpensive, unobtrusive, automated health care solutions. Image PhotoPlethysmoGraphy(IPPG) aids in the development of these solutions by allowing for the extraction of physiological signals from video data. However, the main deficiencies of the recent IPPG methods are non-automated, non-real-time and susceptible to motion artifacts(MA). In this paper, a real-time heart rate(HR) detection method for multiple subjects simultaneously was proposed and realized using the open computer vision(openCV) library, which consists of getting multiple subjects’ facial video automatically through a Webcam, detecting the region of interest (ROI) in the video, reducing the false detection rate by our improved Adaboost algorithm, reducing the MA by our improved compress tracking(CT) algorithm, wavelet noise-suppression algorithm for denoising and multi-threads for higher detection speed. For comparison, HR was measured simultaneously using a medical pulse oximetry device for every subject during all sessions. Experimental results on a data set of 30 subjects show that the max average absolute error of heart rate estimation is less than 8 beats per minute (BPM), and the processing speed of every frame has almost reached real-time: the experiments with video recordings of ten subjects under the condition of the pixel resolution of 600× 800 pixels show that the average HR detection time of 10 subjects was about 17 frames per second (fps).
In contrast to humans, who use only visual information for navigation, many mobile robots use laser scanners and ultrasonic sensors along with vision cameras to navigate. This work proposes a vision-based robot control algorithm based on deep convolutional neural networks. We create a large 15-layer convolutional neural network learning system and achieve the advanced recognition performance. Our system is trained from end to end to map raw input images to direction in supervised mode. The images of data sets are collected in a wide variety of weather conditions and lighting conditions. Besides, the data sets are augmented by adding Gaussian noise and Salt-and-pepper noise to avoid overfitting. The algorithm is verified by two experiments, which are line tracking and obstacle avoidance. The line tracking experiment is proceeded in order to track the desired path which is composed of straight and curved lines. The goal of obstacle avoidance experiment is to avoid the obstacles indoor. Finally, we get 3.29% error rate on the training set and 5.1% error rate on the test set in the line tracking experiment, 1.8% error rate on the training set and less than 5% error rate on the test set in the obstacle avoidance experiment. During the actual test, the robot can follow the runway centerline outdoor and avoid the obstacle in the room accurately. The result confirms the effectiveness of the algorithm and our improvement in the network structure and train parameters