# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 3 Design of the Flexible Wearable Heart Rate Signal Acquisition Module
### Low-Power Algorithm Design of Flexible Wearable Heart Rate Signal Acquisition Module  
The power consumption of algorithms in heart rate signal acquisition modules is also one of the key factors restricting the low-power development of flexible wearable heart rate monitoring systems. To achieve lower power consumption, there is significant room for improvement in algorithm design. Among these, the adjustment of the sampling rate in signal acquisition modules is crucial to balance power consumption and signal acquisition accuracy.  
[PERSON] et al. [68] proposed an algorithm for optimizing the optimal sampling rate of flexible wearable optical sensors through statistical analysis methods. The researchers evaluated the statistically significant differences in PPG and ECG indices at different sampling rates using paired two-tailed t-tests (Bonferroni correction), calculated the average bias and 95% limits of agreement using Bland-Altman analysis to measure consistency, and visualized the correlation between ECG indices and PPG errors through linear regression. The optimal sampling rate of wearable optical sensors was determined by synthesizing these three methods. This algorithm significantly reduces data storage volume and costs while lowering system power consumption.  
In addition to achieving low-power consumption for the heart rate acquisition module by optimizing the sensor sampling frequency, [PERSON] et al. also proposed a dynamic heart rate acquisition interval adjustment algorithm [82] based on multimodal data fusion to reduce the power consumption of the flexible wearable heart rate acquisition module. This algorithm for dynamically adjusting heart rate acquisition intervals first uses multimodal data fusion to identify activity states, for example, distinguishing between scenarios such as sedentary and exercise using accelerometers and gyroscopes, before dynamically mapping acquisition intervals based on a preset rule engine or machine learning models. Specifically, the interval is extended to 5 min during sedentary states to save power, while it is shortened to 10 s during exercise to capture rapid heart rate changes. Furthermore, the algorithm can be integrated with dynamic voltage regulation and data compression technologies to reduce energy consumption, ultimately balancing power consumption and real-time performance while ensuring data accuracy.  
In summary, in the low-power design of algorithms for flexible wearable heart rate signal acquisition modules, using sampling rate optimization algorithms and dynamic heart rate acquisition interval adjustment algorithms are effective approaches to achieve module low-power consumption. Specifically, these methods enable sensors in the heart rate acquisition module to dynamically adjust the optimal sampling rate and acquisition time based on the user's activity state. This dynamic adjustment avoids the high-power consumption causedby continuous high sampling rates. As a result, these methods can strike a balance among module power consumption, signal accuracy, and system performance.