# Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos
## 4 Experimental Results
### Ablation Study  
This section displays the method's ablation study results on the VIPL-HR dataset, which is more complex and allows for an investigation of the impact of each module on experimental performance. 1. Impact of the SS Module: To assess the impact of the SS Module, we designed the following five experiments: 1. We use the multi-attention network proposed in [41] as the feature extraction network to replace the SS module. 2. We use the SS module without an SS block. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MAE** & **RMSE** & **SD** \\ \hline POS [6] & 2.98 & 6.54 & - \\ CHROM [5] & - & 2.24 & - \\ PhysFormer [17] & 11.78 & 15.9 & 10.43 \\ PhysNet [12] & 11.16 & 15.14 & 10.23 \\ [PERSON] et al. [20] & - & 15.35 & - \\ SS-Net & 9.11 & 11.15 & 6.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cross-dataset HR estimation results (training on VIPL-HR and testing in MR-NIRP) of the SS-Net and several SOTA methods. Figure 11: (**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-RGB in the UBFC-rPPG 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-RGB in the UBFC-rPPG dataset. We use the SS module with an SS block, but the SS block does not contain a screener. The screener refers to the coordinate attention. We use the SS module with an SS block, but the SS block does not contain a detector. A detector refers to channel shuffle and dilated convolution. We use the proposed SS module. The results are shown in Table 5. By comparing ablation experiments a and e, it can be observed that ablation experiment e showed a 0.7 MAE advantage in the HR estimation compared with ablation experiment a; the reason for this is that the multi-attention [41] uses pooling layers with different functions on two parallel branches, which makes the module focus not only on global features but also on averages ones, but the attention module based on a convolutional architecture cannot mine long context rhythm clues, which represent subtle changes hidden under skin color as the spatiotemporal dimensions change. Comparing ablation experiments b, c, and d indicates that the screener and detector obtain a more accurate heart rate estimation by aggregating information from multiple spatiotemporal dimensions and focusing on the physiological signals contained in the feature ocean, respectively. Furthermore, by comparing ablation experiments b and e, it can be observed that ablation experiment e performs 0.31 MAE better compared to ablation experiment b. This difference stems from the SS module enhancing discriminative features related to physiological signals across various spatiotemporal coordinates, thereby improving the performance of physiological signal prediction. In addition, we analyzed the input feature size for the SS Module, where the dimensions of the feature maps represent the temporal and spatial directions. The amount of information contained in the feature maps can significantly impact the accuracy of physiological signal estimation. Therefore, we experimented with input features containing different amounts of temporal and spatial information. As shown in Figure 12a, when examining the impact of varying amounts of temporal information, it becomes evident that greater amounts of temporal information lead to smaller errors in heart rate estimation. Specifically, when the input features contain 300 frames (10 s), the MAE reaches 2.86 ppm, with a correlation coefficient r of 0.95. This improvement is attributed to the additional temporal information included in the input feature, which contains more physiological signal information, thereby enhancing heart rate estimation.