# Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos
## 4 Experimental Results
### Ablation Study
Subsequently, to ensure real-time heart rate measurement, we refrained from increasing the amount of temporal information in the feature maps and instead increased the amount of spatial information by reducing the spatial block resolution. As illustrated in Figure 12b, the results demonstrate that increasing the spatial information in the input features leads to degraded heart rate prediction performance. This can be explained by the introduction of redundant information features when increasing spatial information, which introduces noise that adversely affects heart rate estimation. Impact of the SC Module: To assess the impact of the SC module, we compared the performance of the SC module against a CNN-based fusion module, specifically the JAFF module from [35], which introduces an attention mechanism to consider the importance of different locations, feature channels, and modalities. The structure of the network is shown in Figure 13. Additionally, we conducted experiments \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Method** & **MAE** & **RMSE** & **SD** \\ \hline a & Multi-attention [41] & 3.56 & 5.06 & 3.58 \\ b & SS module w/o SS block & 3.17 & 4.96 & 3.81 \\ c & SS module w/o screener & 3.12 & 4.74 & 3.57 \\ d & SS module w/o detector & 3.02 & 4.29 & 3.04 \\ e & SS module & 2.86 & 4.74 & 3.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiments: performance of our proposed SS module. lighter and more informative spatiotemporal feature map (STmap), and the optimal size of the STmap is determined through experiments. Then, the SS module is proposed to extract and aggregate the information of the STmap in different spatiotemporal dimensions and focuses on physiological signals related to heart rate. Then, the MLSC module generates fusion features of RGB and NIR with different semantic levels. Different specific tasks consider the importance of fusion features at different semantic levels to achieve the optimal weight for the specific task. Ultimately, we utilize both multimodal datasets and single-modal datasets to perform various comparison experiments with the other representative approaches. Furthermore, to evaluate our model more objectively, we set up the SS-Net-RGB model. The experimental results demonstrate that our method is effective for non-contact heart rate estimation in complex environments. The proposed SS-Net is of significance for non-contact measurement in less-constrained conditions. Nevertheless, although the suggested approach yields satisfactory results, there remain several limitations. First, the proposed SS-Net has a large number of operation parameters, and more efforts need to be made to make the model lightweight in order to be used on mobile terminals. Second, compared with the intra-dataset test, the results of the cross-dataset test are poor, and the generalization of the SS-Net needs to be further improved. In the future, we hope that the above limitations can be solved to obtain a more robust heart rate measurement. [PERSON], [PERSON] and [PERSON] presented the method; [PERSON] optimized the algorithm; [PERSON] and [PERSON] conducted relevant experiments; [PERSON] conducted data collection work; [PERSON] wrote the manuscript; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] revised and embellished the paper. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data generated during the current study are available from the corresponding author on reasonable request. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.