Non-contact heart rate detection based on weight optimized convolutional neural network
Heart rate is an important indicator of the health and exercise status of the human body.Due to the limitations of traditional heart rate detection,many non-contact detection methods have been proposed in re-cent years,which have good results under cooperative conditions.But the accuracy is significantly reduced when there is motion disturbance.For this problem,this paper combines computer vision and deep learning frontier theory to propose a non-contact heart rate detection method based on weight optimized convolutional neural network.By optimizing the network structure of the convolutional neural network,the noise immunity performance of the network is improved,and a more accurate heart rate value is obtained.Firstly,the stable facial video is input.Secondly,the pixel average of the input facial video frame is taken by frame and row by row,and the time domain expansion is made to obtain each row of sub-pulse waves.The pulse matrix com-posed of the sub-pulse waves is processed by using principal component analysis(PCA)and band-pass filter.Finally,the resulting feature matrix is input into the weight optimized convolutional neural network to learn to predict the heart rate.To verify the performance advantages of this algorithm,2 200 face video samples from the self-collected dataset were used for experimental analysis.The experimental results showed that the proposed algorithm has higher accuracy and better robustness than the existing non-contact heart rate detec-tion methods.
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