# Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos
## 4 Experimental Results
### Intra-Dataset Testing
The blue and red lines represent the ground truth PPG (PPG_gt) and the predicted PPG (PPG_pred) signals, respectively. Figure 7: (**a**) The Bland–Altman plot demonstrating the agreement of the ground truth \(\text{HR}_{\text{gt}}\) and the predicted \(\text{HR}_{\text{pred}}\) for the SS-Net in the VIPL-HR dataset. The lines present the mean and 95% limits of agreement. (**b**) The scatter plot comparing the ground truth \(\text{HR}_{\text{gt}}\) and the predicted \(\text{HR}_{\text{pred}}\) by the SS-Net in the VIPL-HR dataset. (2) Results on the MINIRP-Dataset: Our proposed approach has been verified for effectiveness on the MR-NIRP dataset, and the HR estimate results are shown in Table 2. We compared our method to traditional approaches (CHROM [5], POS [6], those in the work of [PERSON] et al. [21], those in the work of [PERSON] et al. [4]) and deep learning-based methods (PhysNet [12], Physomer [17]). It is clear that deep learning-based methods outperform traditional approaches, consistent with the performance on the VIPL-HR dataset. Additionally, the MAE index of the SS-Net is better than that of other methods, except PhysFormer, which is 0.11 bpm lower than PhysFormer. This indicates that our network exhibits a good correlation in predicting heart rate and reduces outliers effectively. The Bland-Altman plot and scatter plot for HR prediction versus ground truth on the MR-NIRP dataset are shown in Figure 10. The average HR difference is 0.18 bpm, with a 95% consistency range of \(-\)2.93 to 3.30 bpm. The Pearson's correlation coefficient of \(r\) = 0.98 and the coefficient of determination was R\({}^{2}\) = 0.95, demonstrating the robustness and strong generalization ability of our method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MAE** & **RMSE** & **SD** & \(r\) \\ \hline CHROM [5] & - & 2.34 & - & - \\ POS [6] & 2.98 & 6.54 & - & 0.83 \\ [PERSON] et al. [21] & 3.38 & 6.63 & - & - \\ [PERSON] et al. [4] & 2.36 & 4.17 & - & 0.69 \\ PhysNet [12] & 1.32 & 1.91 & - & 0.96 \\ PhysFormer [17] & 1.12 & 1.69 & 1.26 & 0.97 \\ SS-Net-RGB & 1.89 & 2.43 & 1.53 & 0.97 \\ SS-Net & 1.23 & 1.59 & 1.58 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Estimation of HR in the MR-NIRP dataset by the proposed method and several SOTA methods. Figure 9: Comparison of HR estimation on four samples taken from VIPL-HR. The blue and red lines represent the ground truth HR (HR_gt) and the predicted HR values (HR_pred), respectively. However, it is worth noting that while our method achieves competitive results, the gap between our method and PhysNet or Physfomer is not very pronounced. This is attributed to the limited scale of the MR-NIRP dataset, comprising merely 8 participants in contrast to the 107 participants in the VIPL-HR dataset. The dataset's reduced size, insufficient sample variability, and poor robustness render existing heart rate prediction models vulnerable to overfitting, a concern previously highlighted in [19]. Consequently, we aim to assess the efficacy of our model on the more complex UBFC-rPPG dataset. (3) Results on the UBFC-rPPG dataset: We evaluated our proposed method on the UBFC-rPPG dataset, which consists of visible video only, and therefore we used the SS-Net-RGB network for evaluation. The Bland-Altman plot and scatter plot depicting the predicted and true HR on the UBFC-rPPG dataset are shown in Figure 11. The average HR difference was \(-0.36\) bpm, with a 95% consistency range of \(-5.76\) to \(5.03\) bpm. The Pearson's correlation coefficient was \(r=0.98\) and the coefficient of determination was R\({}^{2}=0.97\). As shown in Table 3, even with the use of only visible light videos, our proposed method achieved HR estimation metrics of 2.06 MAE, 2.86 RMSE, and 3.40% MER, which outperformed other deep learning approaches and traditional methods. These results demonstrate the effectiveness of the SS module and validate the points raised earlier, highlighting the superior performance of our model with increasing data sample complexity.