Optimization of PPG Signal Processing based on Hybrid Filtering Technology

With the widespread adoption of wearable devices, pulse oximeters face challenges such as motion artifacts and environmental noise in dynamic scenarios, limiting their accuracy and reliability. This study focuses on optimizing a hybrid filtering framework combining low-pass filtering and LMS adaptive filtering to enhance the performance of photoplethysmography (PPG)-based oximetry. Utilizing a publicly available dataset from Biagetti et al., which includes PPG and triaxial accelerometer data from seven subjects under rest and exercise conditions, we implemented a dynamic low-pass filter with adjustable cutoff frequencies and an LMS algorithm with motion-dependent step size adaptation Comparative experiments with wavelet denoising demonstrated that the proposed framework achieved a signal-to-noise ratio (SNR) of 61.37 dB, a mean squared error (MSE) of 0.0029, and a correlation coefficient (R) of 0.9849, significantly outperforming conventional methods. Results validate the hybrid frameworks effectiveness in suppressing noise while preserving physiological signal integrity, offering a robust solution for wearable health monitoring in dynamic environments.