Remote photoplethysmography (rPPG) is an attractive camera-based health monitoring method that can measure the heart rhythm from facial videos. Many well-established deep-learning models have been reported to measure heart rate (HR) and heart rate variability (HRV). However, most of these models usually require a 30-second facial video and enormous computational resources to obtain accurate and robust results, which significantly limits their applications in real-world scenarios. Hence, we propose a lightweight pulse extraction network, FastBVP-Net, to quickly measure heart rhythm via facial videos. The proposed FastBVP-Net uses a multi-frequency mode signal fusion (MMSF) mechanism to characterize the different modes of the raw signals in a decompose module and reconstruct the blood volume pulse (BVP) signal under a complex noise environment in a compose module. Meanwhile, an oversampling training scheme is used to solve the over-fitting problem caused by the limitations of the datasets. Then, the HR and HRV can be estimated based on the extracted BVP signals. Comprehensive experiments are conducted on the benchmark datasets to validate the proposed FastBVP-Net. For intra-dataset and cross-dataset testing, the proposed approach achieves better performance for HR and HRV estimation from 30-second facial videos with fewer computational burdens than the current well-established methods. Moreover, the proposed approach also achieves competitive results from 15-second facial videos. Therefore, the proposed FastBVP-Net has the potential to be applied in many real-world scenarios with shorter videos.
翻译:远程光谱成像仪(rPPG)是一种有吸引力的基于摄影机的健康监测方法,可以用面部视频测量心脏节奏。许多根深蒂固的深层学习模型被报告用于测量心脏率(HR)和心率变异性(HRV)。然而,大多数这些模型通常需要一个30秒的面部视频和巨大的计算资源,以获得准确和稳健的结果,这大大限制了其在现实世界情景中的应用。因此,我们建议建立一个轻量的脉冲提取网络(FastBVP-Net),以便通过面部视频快速测量心律节奏。拟议的快速BVP-Net(FastBVP-Net)使用多频模式信号聚合(MMSF)机制来描述分解模块中生信号的不同模式,并在一个复杂噪音环境中重建血量脉冲(BVP)信号。同时,一个过度抽样培训计划用于解决拟议数据集局限性造成的超负荷问题。然后,基于提取的 BVP信号,对短期视频-Net(MMMMS-MS-MS-M-MSU)进行综合实验,在基准数据集中进行测试,并用更精确地测试,从目前测试,用HRP-V-V-L-L-L-L-L-Ls-L-VD-L-L-L-L-L-L-L-L-L-S-I-S-S-S-S-S-S-L-S-S-S-L-S-S-S-L-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-