# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
## 4 Discussion  
In this study, we developed a noise reduction algorithm, referred to as the MLFB algorithm, aimed at enhancing pulse signal retrieval from RGB face video datasets. The results demonstrated that the MLFB algorithm outperformed previous methodologies in effectively denoising the pulse signals. The robustness of rPPG against variations in movement is critical because this method relies on video data for signal extraction [30]. Our study further demonstrated the reliability of the proposed algorithm by calculating rPPG from video data collected under four distinct measurement conditions: Resting, Stroop task, Post-workout, and Movement. The improved performance of the MLFB algorithm highlights its potential applicability in various contexts in which accurate pulse signal extraction is crucial, including daily activities, sports, and driving. By enhancing the signal quality, this algorithm can facilitate more reliable cardiovascular assessments based on facial video data. HR variability analysis can also be performed, offering a deeper understanding of the autonomic nervous system that controls homeostatic mechanisms in response to internal and external stimuli [31]. Moreover, the proposed algorithm allows sequential integration with existing pulse extraction methodologies, thereby expanding its applicability across various applications. Although the results of this study were superior, further refinement of the algorithm and exploration of its integration with other techniques to optimize its performance across diverse environments and conditions is necessary. The number of features significantly influences both computational speed and accuracy. Specifically, although an increase in \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metric (\%)**} & \multicolumn{4}{c}{**Combination of Model Features**} \\ \cline{2-5}  & **F1** & **F1 + F2** & **F1 + F2 + F3** & **Fall** \\ \hline Accuracy & 97.97 & 97.84 & 97.81 & 97.91 \\ F1-score & 91.67 & 91.40 & 91.26 & 91.57 \\ Sensitivity & 89.94 & 91.20 & 90.95 & 91.19 \\ Specificity & 99.11 & 98.79 & 98.79 & 98.86 \\ \hline \hline \end{tabular}
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Table 4: Performance of machine-learning models with different feature combinations: phase coherence (F1), POS standard deviation (F2), POS amplitude (F3), and all 11 features (Fall). The values represent averages across all 10 subjects. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Evaluation**} & \multicolumn{4}{c}{**Combination of Model Features**} \\ \cline{2-5}  & **F1** & **F1 + F2** & **F1 + F2 + F3** & **Fall** \\ \hline MAE (bpm) & 2.54 & 2.60 & 2.56 & 2.53 \\ Computational time (s) & 48.38 *** & 48.71 *** & 52.82 *** & 108.28 \\ \hline \hline \end{tabular} Note: bpm, beats per minute.  
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Table 5: Effect of feature selection on the machine learning-based filter bank (MLFB) algorithm performance. Mean absolute error (MAE), computational time, and accuracy were evaluated according to the combination of features of phase coherence (F1), POS standard deviation (F2), POS amplitude (F3), and all 11 features (Fall) listed in Figure 5. Wilcoxon non-parametric test was used to determine a statistically significant difference between the Fall case and other combined feature cases, denoted by ***: \(p<0.001\)). features may enhance accuracy, it concurrently slows down the calculation speed, as summarized in Table 5. This necessitates an optimization strategy for feature selection that balances computational efficiency with signal accuracy when applying the proposed algorithm. Furthermore, the retrieval of rPPG signals is significantly affected by the movement of the subject and the illumination conditions of the measurement environment. Low light conditions challenge reliable video data acquisition, highlighting the need for alternative methodologies to effectively assess facial color changes under such circumstances. Recent advancements have explored the combination of infrared and RGB camera data to address these limitations [32].