Research on non-contact heart rate detection method based on GP-XGBoost

Tianhao Gao; Xu Yang; Zhipeng Ren; Jianping Zhao

doi:10.1117/12.2641431

4 August 2022 Research on non-contact heart rate detection method based on GP-XGBoost

Tianhao Gao, Xu Yang, Zhipeng Ren, Jianping Zhao

Proceedings Volume 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022); 1230616 (2022) https://doi.org/10.1117/12.2641431
Event: Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 2022, Changchun, China

Abstract

In order to improve the problem of inaccurate results in non-contact heart rate detection due to a series of movements of the subject such as breathing, blinking, facial expressions and noise generated by changes in ambient light, the signal is processed in advance using normalisation and wavelet denoising, and then an extreme gradient boosting (XGBoost) algorithm based on a Gaussian process (GP)-based Bayesian optimization method is introduced. The GP-XGBoost machine learning model was introduced to estimate the heart rate. The results show that the estimation error of heart rate by the GP-XGBoost model is significantly reduced compared to that obtained by the conventional method, promoting the practical application of contactless heart rate measurement.

Citation Download Citation

Tianhao Gao, Xu Yang, Zhipeng Ren, and Jianping Zhao "Research on non-contact heart rate detection method based on GP-XGBoost", Proc. SPIE 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 1230616 (4 August 2022); https://doi.org/10.1117/12.2641431

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