# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 4 Design of the Flexible Wearable Heart Rate Signal Preprocessing Module
### Hardware-Algorithm Co-Design of Flexible Wearable Heart Rate Signal Preprocessing Module  
In addition to achieving low-power signal preprocessing operations through the aforementioned hardware and algorithm optimizations, this subsection will introduce more efficient heart rate signal preprocessing via hardware-algorithm co-design. [PERSON] et al. [106] developed a low-power module for heart rate data recognition in flexible wearable systems through hardware-algorithm co-design. In the heart rate data preprocessing stage, the heart rate sensor and microcontroller unit (MCU) provide stable raw heart rate data and time synchronization support for the algorithm. The algorithm, aiming at the characteristic that PPG sensors are vulnerable to motion artifacts, performs normalization with neutral data as the baseline to reduce the impact of hardware acquisition noise. At the low-power optimization level, the system employs low-power PPG sensors and the Bluetooth Low Energy (BLE) protocol, reducing energy consumption through dynamic adjustment of LED brightness and implementation of a sensor sleep mechanism. On the algorithm side, process optimization is carried out: only valid signal segments are transmitted during data transfer, and lightweight moving average filtering is used to minimize computational load and avoid high-power operations. By combining low-power PPG sensors with lightweight moving average filtering, the average power consumption of the system is approximately 14.4 mW, the preprocessing power consumption is reduced by 25%, and the motion artifact suppression rate is improved by 60%. Similarly, [PERSON] et al. [PERSON] et al. (2017) proposed an efficient heart rate data preprocessing module in flexible wearable systems through a hardware-algorithm co-design method. In this system, the hardware GPU undergoes circuit configuration optimization for the Neural-ODE algorithm model to adapt to the computing capability and power consumption constraints of low-power hardware, thereby enhancing the algorithm's operational efficiency on edge devices. The Neural-ODE algorithm, in turn, simplifies the network structure for hardware adaptation, reducing the hardware load. Meanwhile, it performs min-max normalization on heart rate data, efficiently converting large volumes of data into a unified scale to minimize computational complexity and thus reduce overall system power consumption. In summary, the hardware-algorithm co-design for low-power consumption in the heart rate signal preprocessing module has achieved advancements. Through the integration of hardware optimization strategies like the adoption of low-power PPG sensors, dynamic adjustment of LED brightness, and the implementation of sensor sleep mechanisms, alongside algorithmic innovations, such as lightweight moving average filtering, data normalization with neutral baselines, and hardware-adapted Neural-ODE model simplification, the system effectively balances signal processing accuracy and power efficiency. These co-design approaches not only address the challenges of motion artifacts and hardware acquisition noise but also minimize computational load and energy consumption by tailoring algorithms to hardware capabilities and vice versa. The Table 5 lists the innovativeness, practicality, power consumption optimization methods, power consumption levels, and comparisons of power consumption with previous research results for each technology, as shown in Table 5. In the algorithm aspect of the heart rate signal preprocessing module, due to the large number of involved algorithms, this table only selects one traditional algorithm ([PERSON] et al., 2017) and one neural network algorithm ([PERSON] et al., 2017) for list. In the algorithm-hardware co-design aspect, since both references ([PERSON] et al., 2017; [PERSON] et al., 2017) focus on the normalization operation in the heart rate preprocessing process, this table only selects one of them for list.