基于聚类和自适应滤波的成像式心率检测方法  

Imaging Heart Rate Detection Method Based on Clustering and Adaptive Filtering

作　　者：黄漫萍 彭力[1,2] 韩鹏 骆开庆[1,2] 刘冬梅 陈淼[1,2] 邱健 Huang Manping;Peng Li;Han Peng;Luo Kaiqing;Liu Dongmei;Chen Miao;Qiu Jian(School of Electronics and Information Engineering,South China Normal University,Foshan 528225,Guangdong,China;Guangdong Provincial Engineering Research Center for Optoelectronic Instrument,Foshan 528225,Guangdong,China)

机构地区：[1]华南师范大学电子与信息工程学院,广东佛山528225 [2]广东省光电检测工程技术研究中心,广东佛山528225

出　　处：《光学学报》2024年第9期200-210,共11页Acta Optica Sinica

基　　金：国家自然科学基金(61975058,62375089,62205110);广东省自然科学基金联合项目(2022A1515140139);广东省自然科学基金面上项目(2023A1515011452);广东省科技计划(2019B090905005);广州市科技计划(2019050001)。

摘　　要：提出了应用于成像式光电容积描记(IPPG)的凹透镜变形算法和肤色像素聚类的感兴趣区域(ROI)动态选取方法,以及针对脉搏波(BVP)信号的自适应归一化最小均方误差(NLMS)滤波算法来解决IPPG心率检测方法在头部运动及光照变化干扰等运动场景下存在的测量结果准确度低和波动性大等问题。首先,利用凹透镜变形算法将面部肤色区域进行图像扭曲和膨胀。其次,通过K-means++聚类方法进行皮肤区域像素选取。然后,应用CHROM算法对BVP信号进行预降噪处理。上述处理方法构成了ROI动态选取方法,可解决头部运动带来的影响。最后,采用提出的自适应NLMS算法对BVP信号进行光照变化干扰的自适应滤波后,完成心率的计算。实验结果表明,所提出的心率检测方法在运动场景下的平均绝对误差(MAE)达到0.92次/min,即使在光照剧烈变化的条件下MAE也能达到2.20次/min。该方法能够有效解决IPPG技术中ROI定位不准、选取困难以及受光照变化影响严重等不足。Objective In recent years,since heart rate is one of the most important indicators of cardiovascular health,non-contact heart rate measurement methods are highly attractive and popular in daily life.Non-contact imaging photoplethysmography(IPPG)has caught much attention from biomedical researchers due to its non-invasive properties without the need for highperformance hardware devices.However,during non-contact imaging where subjects are less constrained,IPPG measurement results are susceptible to interference from rigid and non-rigid movements such as head turning,smiling,speaking and eyebrow raising,and unstable lighting.For improving the IPPG technique,we propose a region of interest(ROI)selection method with a concave lens deformation algorithm and skin color pixel clustering,and an adaptive normalized least mean square(NLMS)filtering algorithm for blood volume pulse(BVP).The proposed method improves accurate ROI extraction in less constrained conditions and the performance of filtering out non-physiological signal intensity fluctuations in ROI.Meanwhile,it has advantages in accuracy and stability under motion scenes and environments with large illumination variations,holding potential significance for non-contact heart rate monitoring in telemedicine,indoor fitness,psychological testing,and unmanned vehicles.Methods We obtain the subjects heart rates by processing the facial video images.First,the facial skin color region is distorted and expanded by adopting the concave lens deformation algorithm to increase the percentage of the skin pixel region.Next,the K-means++clustering algorithm selects skin pixels again and builds RGB channels to estimate BVP signals.Subsequently,the chrominance-based color space projection decomposition(CHROM)algorithm is applied to pre-denoise the above-mentioned BVP signal.Finally,the proposed adaptive NLMS algorithm is employed to filter out the interference of background light,and then measure heart rate by spectrum analysis.In subsequent experiments,ablation experiments are co

关 键 词：生物技术 成像式光电容积描记 凹透镜变形 K-means++聚类 归一化最小均方误差算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]