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.

翻译：生理感知对在日常生活环境中与人交互的服务机器人、社交机器人和辅助机器人至关重要。远程光电容积描记术（rPPG）可通过RGB摄像头实现非接触式心率（HR）估计，是机器人视觉系统中极具潜力的感知模式。然而，光照变化仍是阻碍其稳健部署的主要障碍。本文提出一种端到端的时空Transformer框架，用于在新构建的多样光照数据集上进行远程心率估计。该估计器集成了基于PRNet的三维人脸对齐、片段级光照增强、残差时序标准化模块及受控混合时频监督机制。训练目标结合了软移位皮尔逊波形损失与频谱Kullback-Leibler散度损失，其中可调权重β控制频域心率引导的贡献度。在覆盖三种光照等级的静态全层级混合协议实验表明，β=5在所有测试参数中取得最优效果，最佳运行心率平均绝对误差（MAE）达0.79 bpm，心率相关系数达0.982。与在我们的数据集上评估的PhysFormer基线相比，本方法将HR MAE降低93.6%，同时将HR相关系数从0.088提升至0.982，使光照变化场景下的实际应用成为可能。