# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 7 Summary and Outlook  
As an innovative integration of flexible electronics and biomedical engineering, flexible wearable heart rate monitoring systems have broken through the limitations of traditional monitoring devices by integrating core technologies, such as ECG and PPG. These systems provide efficient solutions for real-time, continuous, and non-invasive heart rate monitoring. This paper presents a comprehensive review of the system design, core technologies, module designs (including signal acquisition, preprocessing, computation, transmission, and output), as well as key challenges. Notably, it focuses on the universal application of low-power design concepts in hardware selection (e.g., flexible sensors, low-power chips), algorithm optimization (e.g., dynamic sampling rate adjustment, lightweight neural networks), and hardware-algorithm collaboration (e.g., compressive sampling, sleep modes). In the design of the heart rate acquisition module for flexible wearable heart rate monitoring systems, piezoelectric materials, such as PVDF and piezoelectric ceramics, can be utilized. These materials can be employed to fabricate self-powered sensors. These sensors do not require external energy sources, thus achieving a low-power design. At the same time, the sampling frequency of the heart rate acquisition module is adaptively adjusted to achieve the optimal sampling frequency, thereby reducing the energy consumption of the module. For the design of the heart rate preprocessing module, multiple filter circuits can be integrated on a flexible FPC for signal preprocessing. Meanwhile, algorithms such as LMS filtering, wavelet filtering, and neural network algorithms are employed for heart rate signal preprocessing. By reducing noise in the signal through preprocessing, the computational load of the module is minimized, thereby lowering power consumption. In the design of the heart rate computation module, customizing the processor circuit makes the hardware more adapted to the calculation process, improving heart rate calculation efficiency. Combined with lightweight neural network algorithms, it enables more efficient heart rate calculation and reduces module energy consumption. For the design of the heart rate signal transmission module, using low-power Bluetooth modules, Wi-Fi modules, etc., can effectively reduce module power consumption at the hardware level. Meanwhile, matching with efficient compression algorithms reduces power consumption during signal transmission. Looking to the future, the evolutionary path of flexible wearable technologies centered on low-power design is showing distinct features of multidisciplinary integration. At the hardware innovation level, flexible transistor arrays based on organic semiconductors [144] are expected to reduce power density to below 10 nW/mm\({}^{2}\), with this material capable of stable operation over 100,000 cycles while maintaining a bending radius of 10 cm. Meanwhile, three-dimensional integration technology using low-temperature co-fired ceramic (LTCC) processes [145] can reduce the volume of signal conditioning circuits by 60%. When combined with energy harvesting modules (such as triboelectric nanogenerators), it enables all-weather self-powered monitoring. In circuit design, the combination of pulse width modulation (PWM) technology [146] and adaptive bias circuits dynamically adjusts the sampling frequency according to the amplitude of heart rate fluctuations, reducing the sampling rate to 128 Hz in a resting state and automatically increasing it to 512 Hz during exercise, thus achieving intelligent power distribution. Algorithm-level innovations demonstrate the unique advantages of artificial intelligence technologies. A dynamic sampling strategy based on deep learning constructs an adaptive Bayesian model by analyzing the time-domain and frequency-domain distributions of historical heart rate data [147].