# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 4 Design of the Flexible Wearable Heart Rate Signal Preprocessing Module
### Low-Power Algorithm Design of Flexible Wearable Heart Rate Signal Preprocessing Module
Preprocessing of PPG Signals:  
Currently, many researchers have proposed different methods for acquiring, removing, or lessening the impact of motion disturbances in the PPG signals of flexible wearable systems and have used time-domain and frequency-domain signal preprocessing techniques, as well as machine learning-based methods, to estimate heart rates. These algorithms can mainly be classified into two categories. One is the classical signal preprocessing method, and the other is mainly a deep learning-based method. The classical signal preprocessing methods mainly include the CurToSS [103] algorithm and the TAPIR [104] algorithm. As proposed by [PERSON] et al. in the reference [103], the CurToSS algorithm, designed for the preprocessing of heart rate signals in flexible wearable systems, utilizes the sparse spectral decomposition of photoplethysmogram (PPG) signals to identify spectral onset points and track heart rate trajectories. When signal discontinuities are detected, the algorithm triggers a multi-stage reconstruction process. It distinguishes motion artifacts from heart rate components through constrained spectral searches and cross-spectral analysis. Crucially, to achieve low-power operation, CurToSS optimizes the definition of the search range in curve reconstruction to reduce unnecessary computational load. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Environmental Variables** & **Performance Indicators** & **LMS Attenuation Rate (\%)**[98,99] & **NLMS Attenuation Rate (\%)**[98,99] & **WT Attenuation Rate (\%)**[96,100] & **KF Attenuation Rate (\%)**[96,100] \\ \hline Humidity Increased to 40\% RH & THD & \(+\)85.7 & \(+\)60.7 & \(+\)25.0 & \(+\)32.1 \\ \hline Temperature Increased to 35 \({}^{\circ}\)C & SNR & \(-\)18.2 & \(-\)12.5 & \(-\)8.7 & \(-\)7.3 \\ \hline Increased Exercise Intensity & BD & \(+\)360 & \(+\)240 & \(+\)120 & \(+\)150 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of performance attenuation rates of various algorithms under different environmental conditions. Meanwhile, an adaptive parameter selection algorithm based on real-time data features replaces the experience-based parameter settings. This approach reduces computational complexity while maintaining performance, thereby enabling low-power operation. The second method reported by [PERSON] et al. [104] for flexible wearable heart rate monitoring systems is called the TAPIR algorithm. This algorithm achieves low-power consumption by optimizing three cascaded modules: signal decomposition, sparse signal reconstruction, and spectral peak tracking. Specifically, during the signal decomposition stage, the TAPIR algorithm simplifies the screening process of motion artifacts (MA) in singular spectrum analysis (SSA) [105], reducing unnecessary data processing and thus contributing to power reduction. In the sparse signal reconstruction stage, further optimization of the basis matrix pruning strategy improves computational efficiency, effectively reducing power consumption as well. Additionally, in the spectral peak tracking step, the improvement of the method for judging heart rate continuity reduces complex calculations, creating conditions for low-power operation. However, when using classical signal preprocessing methods, as the accuracy of the algorithm improves, the classical signal preprocessing methods are usually accompanied by an increase in the number of free parameters, which is not helpful for achieving low-power consumption. In recent years, some researchers have begun to explore deep learning methods for PPG-based heart rate monitoring. [PERSON] et al. [99] proposed a supervised learning algorithm based on a low-power neural network (NN) in the reference, which is used for the preprocessing of PPG signals in flexible wearable systems. This algorithm achieves low-power consumption by optimizing stages such as candidate peak selection and feature extraction.