机器人需要感知用户心率来调整交互策略，但光照变化一直是部署的拦路虎——这个框架把误差压到了1 bpm以内，做服务机器人或辅助机器人开发的团队可以直接参考。
该论文提出了一种端到端的时空Transformer框架，用于在光照变化条件下通过RGB摄像头远程估计心率（rPPG）。方法结合了3D人脸对齐、光照增强、残差时间标准化模块和混合时频监督，显著提升了机器人环境下的心率估计鲁棒性。在包含三种光照水平的数据集上，该方法将心率平均绝对误差降至0.79 bpm，相关系数达0.982，相比PhysFormer基线误差降低93.6%。这项工作解决了服务机器人在日常光照变化中无法可靠感知用户生理状态的关键问题。
Illumination-Robust Camera-Based Heart-Rate Estimation for Physiological Sensing in Robots
Physiological awareness is important for service, social, and assistive robots that interact with humans in everyday environments. Remote photoplethysmography (rPPG) enables non-contact heart-rate (HR) estimation from an RGB camera, making it a promising sensing modality for robot-mounted vision systems. However, illumination variation remains a major barrier to robust deployment. This paper presents an end-to-end spatial-temporal transformer framework for remote HR estimation on a new dataset with varied illumination. Our estimator integrates PRNet-based 3D face alignment, clip-level illumination augmentation, the Residual Temporal Standardization Module, and controlled hybrid temporal-frequency supervision. The training objective combines a Soft-Shifted Pearson waveform loss with a spectral Kullback-Leibler divergence loss, where a tuned weight ($\mathbfβ$) controls the contribution of frequency-domain heart-rate guidance. Experiments on a static all-level mix protocol covering three illumination levels show that $\mathbfβ=5$ provides the strongest result among the tested beta settings, achieving a best-run HR mean absolute error (MAE) of 0.79 bpm and an HR correlation of 0.982. Compared with the PhysFormer baseline evaluated on our dataset, our estimator reduces HR MAE by 93.6 %, while increasing HR correlation from 0.088 to 0.982, making it usable when illumination varies.