Combining Chrominance Features and Fast ICA for Noncontact Imaging Photoplethysmography

Ken Cai,Hongwei Yue,Weitong Chen,Bohan Li,Wenhua Huang

doi:10.1109/access.2020.2979991

Abstract

Video-based noncontact detection of heart rate has a wide range of applications in the field of medicine and health. However, this method is susceptible to noise interference, making it difficult to effectively extract blood volume pulse (BVP) signals. To overcome this problem, a new method of noncontact heart rate estimation that can suppress noise interference is proposed in this paper. First, the established data acquisition system conducts video collection, and the captured videos are divided into multiple small regions. Subsequently, the initial signals of BVP are extracted in accordance with the chrominance features extracted through multi-channel data fusion. The BVP signals are separated using the FastICA algorithm. The kurtosis value and signal-to-noise ratios of the power spectrum of the separated signals are analyzed to determine the effective separation component. Results show that this method can extract and process pulse signals, effectively suppressing non-periodic interference. The experiment also proves that the method has good consistency with the measurement of pulse oximeter and has good stability and accuracy in the detection of heart rate of the human body.

Highlights

Heart rate is an important indicator that reflects the physiological health of the human body and has important applications in the clinical study of cardiovascular disease [1] and physical exercise [2]
Given that the kurtosis index can quantitatively evaluate the random noise contained in the signal and has the advantages of fast calculation speed, simple algorithm, and strong anti-interference ability, the effective separation component is determined in accordance with the size of the kurtosis value
The heart rate estimation method proposed in this paper considers the effects of incident light, epidermis and subcutaneous reflection, and camera noise; establishes the blood volume pulse (BVP) signal acquisition model; introduces chrominance features and FastICA to eliminate the interference caused by light and movement; and obtains robust heart rate estimation results

Summary

Introduction

Heart rate is an important indicator that reflects the physiological health of the human body and has important applications in the clinical study of cardiovascular disease [1] and physical exercise [2]. The development of noncontact heart rate detection and estimation has become a research hotspot in the field of physiological information monitoring to overcome the limitations of traditional instruments. Recent studies have shown that heart rate can be determined by analyzing skin color changes in video signals. This noncontact and inexpensive method can be used in certain special scenarios, such as skin damage, neonatal care, and imperceptible monitoring situations [6]–[8]. With the continuous development of noncontact measurement technology, imaging photoplethysmography (PPG)iPPG can provide convenience and comfort and reduce medical costs given that it only requires the processing of body skin color from videos to monitor heart rates automatically by means of data fusion and analysis.

Methods

Results

Conclusion