Remote Heart Rate Estimation by Pulse Signal Reconstruction Based on Structural Sparse Representation

被引:11
作者
Han, Jie [1 ]
Ou, Weihua [1 ]
Xiong, Jiahao [1 ]
Feng, Shihua [1 ]
机构
[1] Guizhou Normal Univ, Sch Big Data & Comp Sci, Guiyang 550025, Peoples R China
基金
中国国家自然科学基金;
关键词
heart rate estimation; pulse signal reconstruction; remote photoplethysmography; structural sparse representation; signal processing; health monitoring; PHOTOPLETHYSMOGRAPHY; VALIDATION; NONCONTACT;
D O I
10.3390/electronics11223738
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In recent years, the physiological measurement based on remote photoplethysmography has attracted wide attention, especially since the epidemic of COVID-19. Many researchers paid great efforts to improve the robustness of illumination and motion variation. Most of the existing methods divided the ROIs into many sub-regions and extracted the heart rate separately, while ignoring the fact that the heart rates from different sub-regions are consistent. To address this problem, in this work, we propose a structural sparse representation method to reconstruct the pulse signals (SSR2RPS) from different sub-regions and estimate the heart rate. The structural sparse representation (SSR) method considers that the chrominance signals from different sub-regions should have a similar sparse representation on the combined dictionary. Specifically, we firstly eliminate the signal deviation trend using the adaptive iteratively re-weighted penalized least squares (Airpls) for each sub-region. Then, we conduct the sparse representation on the combined dictionary, which is constructed considering the pulsatility and periodicity of the heart rate. Finally, we obtain the reconstructed pulse signals from different sub-regions and estimate the heart rate with a power spectrum analysis. The experimental results on the public UBFC and COHFACE datasets demonstrate the significant improvement for the accuracy of the heart rate estimation under realistic conditions.
引用
收藏
页数:14
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