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

SignificanceThe performance of wearable biosensors is highly influenced by motion artifacts (MAs).AimWe 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.ApproachThe 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.ResultsThe 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 [−1.98,2.11] bpm, the Pearson correlation coefficient improved from 0.86 to 0.99, and the mean absolute error significantly decreased from 15.12 to 0.76 bpm.ConclusionsWe present an integrated hardware–algorithm co-design, offering a practical solution for high-precision and robust physiological monitoring in ambulatory settings using wearable devices.