# 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
They can better cope with diverse heart rate signal fluctuations and environmental interference, and exhibit high flexibility, thus currently achieving significant progress in the field of heart rate signal computation. However, different neural networks are applicable to different scenarios. For example, lightweight Support Vector Machine (SVM) classifiers are only suitable for scenarios with limited heart rate data, minimal noise interference, and single-task requirements, so not all neural networks are applicable to heart rate monitoring in long-term complex scenarios. Some applicable scenarios of neural networks are presented below. In scenarios requiring robustness in complex noisy environments, when multiple types of noise such as motion artifacts and baseline drift exist, the detection accuracy of neural networks is significantly superior to that of traditional algorithms [115], and they can achieve more reliable heart rate monitoring while maintaining low power consumption. In multi-feature joint detection tasks, when it is necessary to simultaneously detect morphological abnormalities of R-waves, P-waves, and T-waves, neural networks can realize multi-task processing with a single model through end-to-end training, whereas traditional algorithms need to combine multiple modules [108]. In personalized medical scenarios, when model fine-tuning for specific patients is required, neural networks can achieve adaptation by updating only the parameters of the final few layers through transfer learning [116], which is difficult for traditional algorithms to accomplish. However, the parameter size and processing time of some neural networks vary depending on the structure of different neural networks. Some have more parameters, higher power consumption, and longer heart rate signal processing time. Therefore, for monitoring in long-term complex scenarios using flexible wearable heart rate systems, strongly quantized neural networks can be selected, such as lightweight Temporal Convolutional Network (TCN). For example, [PERSON] et al. [117] proposed a lightweight Temporal Convolutional Network (TCN) specifically designed for ECG heart rate abnormality detection in flexible wearable systems through lightweight design. In terms of model architecture, when processing ECG signals, this TCN first expands the signal channels via a 1\(\times\)1 convolutional layer. The residual blocks consist of two dilated convolutional layers, with batch normalization layers, non-linear activation layers, and dropout layers interspersed between them. By stacking these residual blocks, the receptive field of the model can be effectively expanded. The TCN proposed in this study features multiple low-power design characteristics: it has only 14,883 parameters, which is merely 1/27 of the parameter count of the comparable LSTM-FCN model. Its number of multiply-accumulate operations (MACs) is 1,030,260,accounting for only 1/37 of that of the LSTM-FCN, and the power consumption for one run is only 1/12 of that of the LSTM-FCN. This significantly reduces computational complexity and power consumption. In the residual blocks, weight normalization is replaced by batch normalization, and the batch normalization operation is performed at the 1 \(\times\) 1 convolutional layer in the residual branch. Meanwhile, standard dropout is used instead of spatial dropout. These optimization measures avoid complex computations, and through calculations, it is known that the total system power consumption is approximately 1.8 mW. Compared with the existing Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) solutions, the energy consumption is reduced by 19.6 times. Although this study proposes a lightweight Temporal Convolutional Network (TCN), its long-term operational efficiency on energy-constrained devices still requires further evaluation.