用注意力机制提升运动中心率呼吸监测的准确性
Joint Attention Mechanism Learning to Facilitate Opto-physiological Monitoring during Physical Activity
- 通过注意力生成对抗网络建模运动干扰并还原信号
- 运动状态下心率误差低于1.81次/分钟，呼吸率误差2.49次/分钟
- 适合运动健康监测、可穿戴设备研发人员使用
光体积变化描记法（PPG）可无创测量心率与呼吸频率，但运动伪影会严重降低信号质量与估计精度。本文提出一种基于注意力机制的生成对抗网络（AM-GAN），通过融合三轴加速度计获取的运动成分，将受扰动的原始PPG信号重构为去伪影波形。在43名参与者、4种实验协议下，从低强度（6 km/h）到高强度（12 km/h）的运动中验证了该方法。在公开数据集IEEE-SPC上，心率平均绝对误差（MAE）为1.81次/分钟；在PPGDalia上为3.86次/分钟；在自研LU数据集上，心率和呼吸率的MAE分别低于1.37次/分钟和2.49次/分钟。进一步在含三种氧浓度（16%、18%、21%）的自研C2数据集上，血氧饱和度（SpO2）的MAE达1.65%。结果表明，AM-GAN在多种运动强度下均能实现稳定可靠的生理参数估计。
原文摘要 · Abstract (English)
Opto-physiological monitoring including photoplethysmography (PPG) provides non-invasive cardiac and respiratory measurements, yet motion artefacts (MAs) during physical activity degrade its signal quality and downstream estimation concurrently. An attention-mechanism-based generative adversarial network (AM-GAN) was proposed to model motion artefacts and mitigate their impact on raw PPG signals. The AM-GAN learns how to transform motion-affected PPG into artefact-reduced waveforms to align with triaxial acceleration signals corresponding to artefact components gained from a triaxial accelerometer. The AM-GAN has been validated across four experimental protocols with 43 participants performing activities from low to high intensity (6--12km/h). With the public datasets, the AM-GAN achieves mean absolute error (MAE) for heart rate (HR) of 1.81 beats/min on IEEE-SPC and 3.86 beats/min on PPGDalia. On the in-house LU dataset, it shows the MAEs < 1.37 beats/min for HR and 2.49 breaths/min for respiratory rate (RR). A further in-house C2 dataset with three oxygen levels (16%, 18%, and 21%) was applied in the AM-GAN to attain a MAE of 1.65% for SpO2. The outcome demonstrates that the AM-GAN offers a robust and reliable physiological estimation under various intensities of physical activity.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。