浏览全部资源
扫码关注微信
收稿：2019-01-16，
修回：2019-3-31，
录用：2019-4-7，
纸质出版：2019-10-16
移动端阅览
目的
2
心率是反映人体心血管状况和心理状态的重要生理参数。最近的研究表明，光电容积成像技术可以在不接触人体的情况下，利用消费级的摄像机捕获面部表皮颜色的变化进而估计心率。然而，在实际环境中，面部运动带来的干扰会导致心率检测的准确性下降。近年来，国内外学者已经提出了一些方法来去除运动噪声，但是效果均不理想。为了解决上述问题，提出一种可以抗面部运动干扰的新方法。
方法
2
首先检测和跟踪受试者的脸部。然后将脸部区域分块，并提取各块的色度特征建立原始血液容积脉冲矩阵，利用自适应信号恢复算法从原始血液容积脉冲矩阵中分离出低秩矩阵并构建期望血液容积脉冲信号。最后通过功率谱密度估计心率。
结果
2
在环境光作为光源的条件下，利用网络摄像头采集30名受试者的人脸视频进行实验分析。结果显示，提出的方法测得的心率与参考值具有很强的相关性：在静态场景中皮尔森相关系数
$$r$$
=0.990 2，在动态场景中
$$r$$
=0.960 5。并且与最新方法相比，动态场景中的误差率降低了53.90%，相关性提高了7.46%。此外，在10 min的心率检测实验中，方法的测量值与参考值保持着良好的一致性。
结论
2
本文方法优于现有的非接触式心率检测技术，能有效地消除面部运动带来的干扰，长期稳定地检测心率。
Objective
2
Heart rate is an important physiological parameter that reflects the cardiovascular condition and mental state of the human body. Traditional techniques for heart rate detection need pressure sensors or optical sensors attached with human skin. However
the contact between sensors and the human skin tends to result in inconvenience for subjects
especially for those with skin diseases. Accordingly
using traditional techniques in our daily lives is difficult. With the improvement of technology for bio-image information processing
a non-contact heart rate detection method based on imaging photoplethysmography has become an attractive research focus. The color of human epidermis changes in a subtle way with the rhythm of heart beat. These changes are invisible to human eyes and can be captured by a webcam for heart rate estimation. Current technologies for non-contact heart rate detection prioritize facial skin due to its dense capillary distribution and the significant improvement of face tracking technology. However
in realistic environments involving facial motions
the precision of heart rate detected fails to meet the requirements. In recent years
various methods
such as independent principal component analysis
adaptive filtering
and wavelet transform
have been proposed to address this problem. After obtaining initial chrominance signals from different channels of a video
the independent principal component analysis algorithm extracts mutually independent source signals from the initial signals. One of the source signals represents the pulsation of heart beat. However
the source signals obtained are still corrupted by abundant noise. The independent principal component analysis algorithm is complicated
which hinders their widespread application. Adaptive filtering is generally based on the least mean square algorithm. Noises mixed in the chrominance signal can be removed by adaptively adjusting the parameters of the filter
regardless of the noise characteristics. However
this method can merely filter out Gaussian white noise
rather than the sharp noise caused by motion interference in practice. Wavelet transformation method decomposes the original blood volume pulse signal into a series of frequency bands. The method then selects the signals limited in the heart rate band and integrates them into a desirable signal
from which heart rate can be estimated by later processes. Nevertheless
the power spectral density function of the sharp noise caused by motion interference overwhelms the entire heart rate band
which cannot be discarded thoroughly by wavelet transformation. Overall
existing non-contact heart rate detection technologies fail to effectively filter out the sharp noise caused by facial motions
and their results are inaccurate.
Method
2
To tackle above-mentioned problems
this work proposes a novel method for detection of facial motions. Discriminative response map fitting and Kanede-Lucas-Tomasi algorithm are used in video to detect and track the facial regions. Some facial areas (e.g.
eyes) do not contain information about heart rate. Thus the face is divided into several sub-regions
and these sub-regions are then studied respectively
with the aim of mitigating the impact of uninteresting areas. Chrominance characteristics of each sub-region are extracted from the video to establish the raw blood volume pulse matrix. In theoretical aspect
the ideal blood volume pulse matrix should be low-rank. However
in practice
the expression change at a certain moment may make the blood volume pulse distorted abruptly in corresponding time. The rank of the blood volume pulse matrix increase as the result of the distortion of local elements. This work performs the adaptive signal recovery algorithm on the damaged blood volume pulse matrix to discard abnormal elements arising from facial motions and reconstruct the row-rank matrix. From the matrix
sub-regions rich in information about heart rate are selected to compose the desirable blood volume pulse signal. Heart rate can be calculated according to the power spectrum density of the desirable blood volume pulse signal.
Result
2
About 211 videos from 30 subjects are recorded under natural ambient lighting condition. Three series of experiments are conducted on the proposed method to verify its validity:experiments focusing on heart rate accuracy in static scenarios and dynamic scenarios
experiments focusing on method stability with different video durations and frame rates
and a 10 minutes experiment for long-term heart rate monitoring. Results of the accuracy experiments show that the Pearson correlation coefficient between the estimated heart rate and the ground truth is 0.990 2 in static scenarios and 0.960 5 in dynamic scenarios. The heart rate estimated by the proposed method highly approximates to the ground truth. Compared with the newest method
our method decreases the error rates by 53.90% and increases the Pearson correlation coefficient by 7.46%. The stability experiments show that the proposed method performs well as long as the duration of videos is higher than 8 seconds or the frame rate is higher than 20 frames per second. The long-term heart rate monitoring shows that the heart rate measured by the proposed method has similar fluctuations with the ground truth.
Conclusion
2
The proposed method
which is proved to be superior to state-of-the-art ones
can dispose of expression disturbances and increase the detection accuracy of heart rate. However
in cases involving sudden illumination changes
the accuracy of the proposed method fails to reach the high level
which is planned to be further improved in our future works.
Wu X S. Significance of heart rate in cardiovascular disease[J]. Chinese Journal of Internal Medicine, 2006, 45(7):601-602.
吴学思.心率在心血管疾病中的意义[J].中华内科杂志, 2006, 45(7):601-602. [DOI:10.3760/j.issn:0578-1426.2006.07.033]
Mazomenos E B, Biswas D, Acharyya A, et al. A low-complexity ECG fea ture extraction algorithm for mobile healthcare applications[J]. IEEE Journal of Biomedical and Health Informatics, 2013, 17(2):459-469.[DOI:10.1109/TITB.2012.2231312]
Hanning C D, Alexander-Williams J M. Pulse oximetry:a practical review[J]. BMJ, 1995, 311(7001):367-370.[DOI:10.1136/bmj.311.7001.367]
Lim Y G, Kim K K, Park S. ECG measurement on a chair without conductive contact[J]. IEEE Transactions on Biomedical Engineering, 2006, 53(5):956-959.[DOI:10.1109/TBME.2006.872823]
Wilkins M C. Residual bacterial contamination on reusable pulse oximetry sensors[J]. Respir Care, 1993, 38(11):1155-1160.
Sun Y, Thakor N. Photoplethysmography revisited:from contact to noncontact, from point to imaging[J]. IEEE Transactions on Biomedical Engineering, 2016, 63(3):463-477.[DOI:10.1109/TBME.2015.2476337]
Verkruysse W, Svaasand L O, Nelson J S. Remote plethysmographic imaging using ambient light[J]. Optics Express, 2008, 16(26):21434-21445.[DOI:10.1364/OE.16.021434]
Poh M Z, McDuff D J, Picard R W. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation[J]. Optics Express, 2010, 18(10):10762-10774.[DOI:10.1364/OE.18.010762]
Poh M Z, McDuff D J, Picard R W. Advancements in noncontact, multiparameter physiological measurements using a webcam[J]. IEEE Transactions on Biomedical Engineering, 2011, 58(1):7-11.[DOI:10.1109/TBME.2010.2086456]
Fan Q. Study on key techniques of non-contact detection of cardiovascular physiological parameters[D]. Wuhan: Wuhan University, 2017. http://cdmd.cnki.com.cn/Article/CDMD-10486-1018033130.htm .
范强.心血管生理参数非接触式检测关键技术研究[D].武汉: 武汉大学, 2017.
Li X B, Chen J, Zhao G Y, et al. Remote heart rate measurement from face videos under realistic situations[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognitio n. Columbus, OH, USA: IEEE, 2014: 4264-4271.[ DOI: 10.1109/CVPR.2014.543 http://dx.doi.org/10.1109/CVPR.2014.543 ]
Kumar M, Veeraraghavan A, Sabharwal A. DistancePPG:robust non-contact vital signs monitoring using a camera[J]. Biomedical Optics Express, 2015, 6(5):1565-1588.[DOI:10.1364/BOE.6.001565]
Liu Y, Ouyang J F. Non-contact method of heart rate measurement based on facial video[J]. Nanotechnology and Precision Engineering, 2016, 14(1):76-79.
刘祎, 欧阳健飞.基于人脸视频的非接触式心率测量方法[J].纳米技术与精密工程, 2016, 14(1):76-79. [DOI:10.13494/j.npe.20140108]
Qi G, Yang X Z, Wu X, et al. Heart rate detection for non-cooperative shaking face[J]. Journal of Image and Graphics, 2017, 22(1):126-136.
戚刚, 杨学志, 吴秀, 等.非合作面部晃动情况下的心率检测[J].中国图象图形学报, 2017, 22(1):126-136. [DOI:10.11834/jig.20170114]
Viola P, Jones M. Rapid object detection using a boosted cascade of simple features[C]//Proceedings of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai, HI, USA: IEEE, 2001.[ DOI: 10.1109/CVPR.2001.990517 http://dx.doi.org/10.1109/CVPR.2001.990517 ]
Asthana A, Zafeiriou S, Cheng S Y, et al. Robust discriminative response map fitting with constrained local models[C]//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR, USA: IEEE, 2013: 3444-3451.[ DOI: 10.1109/CVPR.2013.442 http://dx.doi.org/10.1109/CVPR.2013.442 ]
Tomasi C, Kanade T. Shape and motion from image streams under orthography:a factorization method[J]. International Journal of Computer Vision, 1992, 9(2):137-154.[DOI:10.1007/BF00129684]
de Haan G, Jeanne V. Robust pulse rate from chrominance-based rPPG[J]. IEEE Transactions on Biomedical Engineering, 2013, 60(10):2878-2886.[DOI:10.1109/TBME.2013.2266196]
Wright J, Ganesh A, Rao S, et al. Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization[C]//Proceedings of Advances in Neural Information Processing Systems. Vancouver, Canada: NIPS, 2009: 2080-2088.
Lin Z C, Chen M, Wu L Q, et al. The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices[R]. Illinois: Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, 2009.
Lin Z C, Liu R S, Su Z X. Linearized alternating direction method with adaptive penalty for low-rank representation[C]//Proceedings of the 24th International Conference on Neural Information Processing Systems. Granada, Spain: Curran Associates Inc., 2011: 612-620.
Akritas A G, Malaschonok G I. Applications of singular-value decomposition (SVD)[J]. Mathematics and Computers in Simulation, 2004, 67(1-2):15-31.[DOI:10.1016/j.matcom.2004.05.005]
Candès E J, Li X D, Ma Y, et al. Robust principal component analysis?[J]. Journal of the ACM, 2011, 58(3):11.[DOI:10.1145/1970392.1970395]
Septiana L, Hariyanto F, Lin K P. Comparison of independent component analysis (ICA) algorithm for heart rate measurement based on facial imaging[M]//Jaffray D A. World Congress on Medical Physics and Biomedical Engineering. Cham: Springer, 2015: 215-219.[ DOI: 10.1007/978-3-319-19387-8_52 http://dx.doi.org/10.1007/978-3-319-19387-8_52 ]
Adler J, Parmryd I. Quantifying colocalization by correlation:the Pearson correlation coefficient is superior to the Mander's overlap coefficient[J]. Cytometry Part A, 2010, 77(8):733-742.[DOI:10.1002/cyto.a.20896]
Yao L F. Non-contact heart rate measurement using PPG and color videos[D]. Tianjin: Tianjin University, 2012. http://d.wanfangdata.com.cn/Thesis/D322455 .
姚丽峰.基于PPG和彩色视频的非接触式心率测量[D].天津: 天津大学, 2012.
相关文章
相关作者
相关机构
京公网安备11010802024621
