Research on heart rate extraction method based on mobile phone video

• Published in: Medical engineering & physics 2023-10, Vol.120, p.104051-104051, Article 104051
• Main Authors: Yao, An, Chou, Yongxin, Yang, Liming, Hu, Linqi, Liu, Jicheng, Gu, Suhang
• Format: Article
• Language: English
• Subjects: Heart rate; Improved automatic multiscale-based peak detection; Mobile phone video; Pulse signal
• ISSN: 1350-4533; 1873-4030
• DOI: 10.1016/j.medengphy.2023.104051

•Extract the pulse signal and calculate the heart rate through the phone camera.•An improved adaptive peak extraction algorithm is proposed to detect the peaks of pulse signal.•The proposed method makes an important contribution in improving the accuracy of heart rate calculation and reducing the time consumption. As an important indicator of human health, heart rate is related to the diagnosis of cardiovascular diseases. In recent years, extracting the heart rate from the mobile phone image has become a research hotspot. However, the illumination intensity of the background, frame rate of the video, and resolution of the image influence heart rate detection accuracy. To overcome these problems, this study proposed a novel heart rate extraction method based on mobile video. Firstly, the mobile phone camera is engaged to record the finger video, the region of interest (ROI) is extracted through the iterative threshold, and the pulse signal is obtained according to the grayscale change of the resolution within the ROI. Then, a low-pass and a high-pass Butterworth filters are exploited to filter out the noise and interframes from the extracted pulse signal. Finally, an improved adaptive peak extraction algorithm is proposed to detect the pulse peaks and the heart rate derived from the difference in pulse peaks. The experimental results show that light intensity, frame rate and resolution all have an influence on the heart rate extraction accuracy, with the most obvious influence of light, the average accuracy of the experiment can reach 99.32 % under good lighting conditions, while only 72.23 % under poor lighting conditions. In terms of frame rate, increasing the frame rate from 30 fps to 60 fps, the accuracy is improved by 0.9 %. For the resolution, increasing the resolution from 1080 p to 2160 p, the accuracy is improved by 1.12 %. While comparing the proposed method with existing methods, the proposed method has a higher accuracy rate, which has important practical value and application prospects in telemedicine and daily monitoring.