# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 5 Design of the Flexible Wearable Heart Rate Signals Computation Module
### Low-Power Algorithm Design of the Computation Module for Flexible Wearable Heart Rate Signals
To this end, this paper collates and compares the computational overhead of this TCN with that of the Pan-Tompkins algorithm, Threshold Detection Algorithm, LSTM-FCN algorithm, and Lightweight Support Vector Machine (SVM) Classifier. The reason for selecting these algorithms for comparison is as follows: the Pan-Tompkins algorithm and Threshold Detection Algorithm are classic methods in the field of heart rate monitoring, characterized by simple computation and low power consumption, serving as important references for lightweight design; the Lightweight SVM Classifier is representative among traditional machine learning algorithms; and the LSTM-FCN algorithm represents the mainstream architecture in deep learning. The comparison results are shown in Table 6. Data indicate that R-wave detection based on the Pan-Tompkins algorithm requires only 0.8 milliseconds of computation time per heartbeat, with a power consumption of 0.25 milliwatts [113]. In contrast, the inference process of the TCN model requires 12.5 milliseconds and 1.8 milliwatts [117], which are 15.6 times and 7.2 times those of the Pan-Tompkins algorithm, respectively. The Threshold Detection Algorithm has a signal processing time of 0.5 milliseconds, with a power consumption of 0.18 milliwatts [114]; while the Lightweight SVM Classifier has a signal processing time of 4.3 milliseconds and a power consumption of 0.75 milliwatts [118]. It is evident that the Pan-Tompkins algorithm, Threshold Detection Algorithm, and Lightweight SVM Classifier all outperform the lightweight TCN model in terms of signal processing time and power consumption. However, this is because the Pan-Tompkins algorithm, Threshold Detection Algorithm, and Lightweight SVM Classifier have relatively simple computational logic, without the need for a large number of parameters and complex calculations. Meanwhile, the Lightweight SVM Classifier is only applicable to scenarios with limited heart rate data, minimal noise interference, and single-task requirements [118], whereas the TCN is not only suitable for the aforementioned simple scenarios but also performs excellently in heart rate monitoring in complex environments and heart rate monitoring scenarios with multi-task objectives. Finally, compared with the LSTM-FCN algorithm, which has a signal processing time of 58.4 milliseconds and a power consumption of 21.6 milliwatts [117], the lightweight TCN still has obvious advantages. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Algorithm Type** & **Single Processing Time (ms)** & **Power Consumption (mW)** & **Number of Parameters** \\ \hline Pan–Tompkins Algorithm [113] & 0.8 & 0.25 & N/A \\ \hline Threshold Detection Algorithm [114] & 0.5 & 0.18 & 7 \\ \hline Lightweight SVM Classifier [118] & 4.3 & 0.75 & 641 \\ \hline LSTM-FCN model [117] & 58.4 & 21.6 & 404,741 \\ \hline Lightweight TCN (Temporal CNN) [117] & 12.5 & 1.8 & 14,883 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of computational overhead of different heart rate calculation algorithms. In summary, whether it is the low power consumption achieved by traditional heart rate calculation algorithms through coefficient optimization, precise setting of statistical ranges, and approximate computation, or the low power consumption accomplished by neural networks via structural simplification and model quantization, these methods collectively provide a wealth of effective strategies for the low-power design of algorithms used in flexible wearable heart rate signal computation. Meanwhile, it should be noted that although neural network algorithms such as TCN are sometimes inferior to traditional hard-coded algorithms like the Pan-Tompkins algorithm and Threshold Detection Algorithm and the Lightweight SVM Classifier in terms of power consumption and response time, they have greater advantages in heart rate monitoring in complex scenarios and multi-task target scenarios, with stronger robustness.