# 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  
In addition to hardware design, the low-power design of algorithms for the heart rate signal computation module is also of great significance. At the same time, neural network technology has demonstrated great potential in improving the accuracy and efficiency of heart rate computation. Next, this subsection will explore how to leverage traditional heart rate computation algorithms and neural network algorithms to achieve a low-power algorithm design for the heart rate signal computation module while also balancing the accuracy of heart rate signal computation. Traditional algorithms refer to algorithms that, distinct from neural network algorithms, are based on explicit mathematical models, rely on manually designed features, have low computational complexity, and offer strong interpretability [110]. In terms of traditional heart rate computation algorithms, [PERSON] et al. [111] proposed a low-power statistical FFT signal analysis method for flexible wearable systems, which determines heart rate by counting the occurrences of indices within the cardiac frequency range after FFT transformation. In practical applications, clearly defining statistical ranges and presetting appropriate heart rate frequency search intervals according to specific application scenarios can effectively reduce unnecessary statistical computations, resulting in a system power consumption as low as 2.4 milliwatts. Compared with traditional FFT signal analysis algorithms, its power consumption is reduced by more than 40%. Additionally, optimizing the storage method of statistical results by reducing storage space occupancy and minimizing read/write operations also helps to save system power consumption. Furthermore, [PERSON] et al. [112] proposed a low-power Maximum Likelihood (ML) prediction algorithm for flexible wristband systems, which estimates heart rate by constructing and maximizing likelihood functions. During algorithm optimization, approximation methods are employed to simplify complex likelihood function computations and reduce the execution frequency of computationally intensive operations, thereby reducing power consumption while maintaining an acceptable level of precision loss. Moreover, in the iterative optimization process, rational configuration of iteration termination criteria avoids excessive iterations, thereby eliminating redundant computations and ultimately achieving the goal of power reduction. In addition, in traditional algorithms, the Pan-Tompkins Algorithm and Threshold Detection Algorithm are also very classic heart rate computation methods. For example, [PERSON] et al. [113] proposed an optimized Pan-Tompkins Algorithm. By optimizing the QRScomplex localization method, adopting adaptive threshold and backtracking technology, etc., it can quickly locate the P-wave position and accelerate the heart rate calculation speed, thereby reducing the system power consumption to 0.25 mW. [PERSON] et al. [114] proposed a Threshold Detection Algorithm, which uses a dual-threshold mechanism and dynamically adjusts the thresholds according to signal characteristics to adapt to different heart rate signal scenarios. The high threshold (THR1) is used for initial signal analysis. If no QRS complex is detected within a specific time, the low threshold (THR0) is activated, and the threshold is adaptively updated through historical peak data, resulting in a low system operating power consumption of 0.18 mW. Traditional heart rate calculation algorithms, such as the Pan-Tompkins Algorithm and Threshold Detection Algorithm, often rely on manually designed feature extraction rules. They have poor adaptability to complex environments, tend to suffer from decreased heart rate monitoring accuracy in complex dynamic scenarios, and lack flexibility. In contrast, neural networks, with their strong self-learning capabilities, can autonomously mine deep-seated features from raw signals and possess excellent adaptive adjustment mechanisms.