# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
## 3 Results
### Evaluating MAE by Window Size  
The MAE of HR estimated using the previously reported no pre-processing (none), BPF, and ASF algorithms, as well as the proposed MLFB algorithm, is represented in Figure 4. The window size was linearly divided into 40 s intervals, starting from the shortest time containing the blood flow signal, and time intervals of 20, 60, and 100 s were used to drive the HR estimation from each rPPG. The MLFB algorithm approach resulted in significantly (*: \(p\) < 0.05, **: \(p\) < 0.01, and ***: \(p\) < 0.001) lower errors than the other approaches. As the window size increased, the errors decreased. However, a longer window size required more stacked rPPG data, and the HR updated it less frequently.  
Table 2 summarizes the MAE of HR according to the combined approach of pulse extraction algorithm (G [4], G-R [28], PCA [15], ICA [14], CHROM [12], PBV [11], POS [13]), and noise reduction algorithm (none, BPF, ASF, and MLFB). Because the evaluation results showed significant differences with a window size of 60 s, the data were evaluated using a 60 s window. When the video data were processed using a combination of the POS and MLFB algorithms, the calculated HR exhibited the lowest MAE of 2.53 bpm. The signal-to-noise ratios for each case in Table 2 are summarized in Table 3. The combination of POS and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Mean Absolute Error of Heart Rate**} \\ \hline \multicolumn{6}{c}{**Noise Reduction**} & \multicolumn{6}{c}{**Pulse Extraction Algorithm**} \\ \cline{2-7}
**Algorithm** & **G** & **G-R** & **PCA** & **ICA** & **CHROM** & **POS** \\ \hline None & 14.80 *** & 10.15 * & 15.12 *** & 10.86 ** & 4.61 ** & 3.92 ** \\ BPF & 10.60 * & 8.14 & 11.62 & 7.82 & 4.43 ** & 3.53 * \\ ASF & 14.73 *** & 10.13 * & 7.97 & 8.48 & 4.27 * & 3.75 \\ MLFB & 8.32 & 9.83 & 9.41 & 7.19 & 2.68 & 2.53 \\ \hline \hline \end{tabular} Note: G, green; G-R, green–red; PCA, principal component analysis; ICA, independent component analysis; CHROM, chrominance-based method; POS, plane-to-orthogonal-Skin; BPF, band-pass filtering; ASF, amplitude selective filtering; MLFB, machine learning-based filter bank.  
\end{table}
Table 2: Mean absolute error of heart rate is computed by the combination of six pulse extraction and four noise reduction algorithms (see the text for more details of each approach). Statistical differences between the results of the machine learning-based filter bank (MLFB) and other algorithms within the same pulse extraction algorithm were evaluated using the Wilcoxon non-parametric test (denoted by *: \(p\) < 0.05, **: \(p\) < 0.01, and ***: \(p\) < 0.001).  
Figure 4: Comparison of the mean absolute error of heart rate retrieved by four different algorithms with three window sizes: no pre-processing (none) in blue, band-pass filter (BPF) in white, amplitude-selective filter (ASF) in gray, and machine learning-based filter bank (MLFB) in pink. Statistically significant difference is denoted by *: \(p\) < 0.05, **: \(p\) < 0.01, and ***: \(p\) < 0.001.  
the proposed MLFB algorithms resulted in the highest value of \(-\)1.02 dB. Among the four noise reduction algorithms, the MLFB effectively processed noise in most cases, thereby improving signal quality.