# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 6 Design of the Flexible Wearable Heart Rate Signals Transmission and Output Module
### Low-Power Algorithm Design for Flexible Wearable Heart Rate Signal Transmission and Output Module  
In the field of flexible wearable heart rate monitoring, the low-power design of algorithms for heart rate signal transmission and output is also of utmost importance. By applying efficient compression algorithms, such as Huffman coding, heart rate data can be accurately compressed to remove redundancy. At the same time, through the optimization of communication protocols, transmission parameters are dynamically adjusted. While ensuring the stable transmission of data, power consumption is effectively reduced, achieving the goal of low-power consumption at the algorithm level. Generally speaking, data compression methods are divided into two categories: lossy compression and lossless compression. Lossy methods are widely used in the compression of electrocardiogram signals due to their high compression ratio performance. Previous research in this area can be classified into three categories. Firstly, calculate the redundancy of the signal and then remove the redundancy to obtain a higher data compression ratio. The second is the transformation method, which converts the time-domain signal into the frequency domain or other domains and compresses its spectrum or energy components. The third is the parameter extraction compression method, including the peak extraction method and the linear prediction method. As reported by [PERSON] et al., there is a DCT-based real-time ECG data compression and transmission algorithm for flexible wearable systems [123]. The system design is shown in Figure 6b. This algorithm is designed to address the challenges of efficient ECG data transmission and power consumption. The framework consists of five compression procedures and four reconstruction modules. During compression, the ECG signal undergoes 1/2 downsampling followed by backward difference computation. Subsequent peak detection in the differential signal partitions the data into segments for storage. The stored data then undergoes Discrete Cosine Transform (DCT), window-based filtering, and Huffman encoding for output. Reconstruction reverses these operations through Huffman decoding, inverse filtering, Inverse DCT (IDCT), and peak-point-based signal restoration. On the basis of the research by [PERSON] et al., [PERSON] et al. innovatively reported a DCT-IV compression algorithm with variable transform length [71], which is used to improve the transmission efficiency of heart rate signals and reduce the power consumption of the flexible wearable system. The compression and decompression processes are shown in Figure 6c. The ECG compression algorithm reduces data processing complexity and computational energy consumption through backward difference calculation and non-uniform quantization, while its intelligent encoding of sign bits eliminates redundant operations. Compared with the traditional DCT-IV method, the algorithm effectively reduces data volume by 38.7% while suppressing the dynamic range of physiological signals. Meanwhile, the high compression ratio of the algorithm improves wireless transmission efficiency, thereby reducing communication energy consumption. For wearable cardiac monitoring devices, it can reduce the dynamic range requirement by 34.7% and keep the system power consumption below 5.0 mW, ensuring both energy efficiency and clinical value. In addition to compressing heart rate signals using the above-mentioned optimized compression algorithms to reduce data redundancy and thus lower power consumption, power consumption can also be reduced by optimizing communication protocols. For example, adaptive modulation techniques [136; 137; 138] can be adopted to dynamically adjust transmission parameters according to channel conditions and data transmission requirements. For example, [PERSON] et al. [139] proposed a signal transmission and output module based on the Dynamic Feedback-based Adaptive Modulation (DF-AM) algorithm to achieve the low-power design of flexible wearable heart rate systems.