Color-filter-array-based multispectral photoplethysmography optical sensor and its motion artifact correction algorithm.

Significance: The performance of wearable biosensors is highly influenced by motion artifacts (MAs). Aim: We propose a motion artifact removal algorithm using blind source separation-multi-stage least mean square adaptive filtering with multi-wavelength photoplethysmography (PPG) signals to enable accurate physiological parameter estimation in wearable devices. Approach: The algorithm is implemented with a custom-designed PPG sensor that enables synchronized multi-wavelength acquisition via a compact optical design integrated with a color filter array. The algorithm exploits the high correlation of MA components across wavelengths to autonomously generate a noise reference in real time through blind source separation. Furthermore, a frame-level quality assessment mechanism based on power spectral entropy is introduced, which dynamically evaluates the interference level according to the entropy value and intelligently switches between two pre-optimized sets of filter parameters. This allows for dynamic parameter adjustment of the MSLMS filter, thereby effectively tracking and suppressing motion artifacts without the need for external inertial sensors. Results: The performance of the proposed algorithm was evaluated in a study involving 13 subjects performing free-arm swings to simulate daily motion. Experimental results demonstrate that after algorithm processing, the limits of agreement between the estimated heart rate and the electrocardiogram reference values narrowed from [3.09, 26.94] to Conclusions: We present an integrated hardware-algorithm co-design, offering a practical solution for high-precision and robust physiological monitoring in ambulatory settings using wearable devices.