# 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
Adaptive filtering (LMS/NLMS) achieves a better balance between performance and low-power consumption. Specifically, the LMS algorithm yields an MSE = 0.021 and an SNR = 28.5 dB in the resting state, and an MSE = 0.089 and an SNR = 20.1 dB in the running state. In contrast, the NLMS algorithm exhibits an MSE = 0.019 and an SNR = 29.2 dB in the resting state, and an MSE = 0.078 and an SNR = 21.0 dB [94; 95] in the running state. These results indicate that adaptive filtering algorithms are more suitable for flexible wearable system scenarios. In addition to the impact of motion states on algorithms, this paper also considers the quantifiable effects of environmental factors on algorithm performance, as shown in Table 4. References [98; 99] present a comparison between the LMS attenuation rate and the NLMS attenuation rate, while references [96; 100] provide a comparison between the WT attenuation rate and the KF attenuation rate. Among these, references [96; 98] focus on research related to wearable systems. When skin humidity increases to 40% \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Filtering Algorithm** & **Resting State** & **Running State** & **Power Consumption (mW)** \\ \hline LMS Adaptive Filtering [94; 95] & MSE = 0.021, SNR = 28.5 dB & MSE = 0.089, SNR = 20.1 dB & 2.3 \\ \hline NLMS Adaptive Filtering [94; 95] & MSE = 0.019, SNR = 29.2 dB & MSE = 0.078, SNR = 21.0 dB & 2.5 \\ \hline Wavelet Filtering [96; 97] & MSE = 0.017, SNR = 30.1 dB & MSE = 0.065, SNR = 22.3 dB & 3.1 \\ \hline Kalman Filtering [96; 97] & MSE = 0.023, SNR = 27.9 dB & MSE = 0.095, SNR = 19.5 dB & 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of filtering algorithm performance under different motion states. RH, the Total Harmonic Distortion (THD) attenuation rates of the LMS algorithm and the NLMS algorithm are extremely high, being 85.7% and 60.7% [98,99], respectively, while the THD attenuation rates of the Wavelet Transform (WT) algorithm and the Kalman Filter (KF) algorithm are relatively low, being 25.0% and 32.1% [96,100], respectively, indicating better performance. When the temperature rises to 35 \({}^{\circ}\)C, the Signal-to-Noise Ratio (SNR) attenuation rates of the LMS algorithm and the NLMS algorithm are \(-\)18.2% and \(-\)12.5% [98,99], respectively, while the SNR attenuation rates of the Wavelet Transform (WT) algorithm and the Kalman Filter (KF) algorithm are \(-\)8.7% and \(-\)7.3% [96,100], respectively. Among them, the Kalman Filter (KF) algorithm has the lowest SNR attenuation rate and shows the best performance. Similarly, in the case of increased exercise intensity, by comparing the data of each algorithm in the table, it can be known that the Bit Error Rate (BD) attenuation rate of the Wavelet Transform (WT) algorithm is the lowest, indicating the best performance. Overall, different algorithms have different performance under different environmental conditions. The Kalman Filter (KF) algorithm performs prominently when the temperature increases, while the Wavelet Transform (WT) algorithm performs better when humidity increases and exercise intensity increases. In summary, for ECG signal preprocessing, in addition to adaptive filtering algorithms, such as LMS and NLMS, methods such as WT [101] and KF [102] can also be employed. Each of these algorithms has its own advantages and disadvantages in ECG signal preprocessing. In practical applications, it is necessary to reasonably select or combine these algorithms based on motion scenarios, environmental factors, noise characteristic requirements, and system resource limitations. This approach ensures efficient and accurate preprocessing of ECG signals while taking into account the design of the system's power consumption.