Respiration Rate Estimation from Remote PPG via Camera in Presence of Non-Voluntary Artifacts

2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2022-09-27 DOI:10.1109/BSN56160.2022.9928485

K. Vatanparvar, Migyeong Gwak, Li Zhu, Jilong Kuang, A. Gao

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引用次数: 1

Abstract

Contactless measurement of vitals has been seen as a promising alternative to contact sensors for monitoring of health condition. In this paper, we focus on respiration rate (RR) as one of the fundamental biomarkers of a person’s cardio and pulmonary activities. Remote RR estimation has gained attraction due to its various potential applications; use of RGB cameras to extract remote photoplethysmography (PPG) signal from subjects’ face has been debated as one of the enabling technologies for remote RR estimation. The technology is challenged with respect to wide range of RR and non-voluntary motion in uncontrolled settings. We propose a novel methodology to enhance the remote PPG signal and remove artifacts from the respiration signal. The method achieves 3.9bpm MAE of 90% percentile (1.3bpm decrease) for estimating RR in range of 5-25bpm. We validate the performance using smartphone video recordings of 30 subjects with uniform distribution of skin tone.

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在非自愿伪影存在下，通过相机从远程PPG估计呼吸速率

非接触式生命体征测量已被视为一种有前途的替代接触式传感器监测健康状况。在本文中，我们重点关注呼吸速率(RR)作为一个人的心肺活动的基本生物标志物之一。远程RR估计因其各种潜在的应用而受到关注;使用RGB相机从受试者的面部提取远程光电体积脉搏波(PPG)信号作为远程RR估计的使能技术之一一直存在争议。该技术面临的挑战是在不受控制的环境下，相对于大范围的RR和非自愿运动。我们提出了一种新的方法来增强远程PPG信号并去除呼吸信号中的伪影。该方法对5-25bpm范围内的RR进行估计，MAE达到3.9bpm，降低了90%的百分位数(降低了1.3bpm)。我们使用30名肤色均匀分布的受试者的智能手机视频记录来验证性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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