Key result
Redundant denoising and adaptive filtering improves oxygen saturation estimation to ~94% in motion-contaminated signals.
Why the study?
Motion artifacts remain a primary limitation affecting the reliability of continuous wearable monitoring of oxygen saturation and non-invasive blood pressure in ambulatory environments.
Does a novel denoising framework using redundant sensing and adaptive filtering improve signal quality and physiological parameter estimation in wearable PPG and NIBP monitoring under motion conditions in healthy volunteers?
Does a novel denoising framework using redundant sensing and adaptive filtering improve signal quality and physiological parameter estimation in wearable PPG and NIBP monitoring under motion conditions in healthy volunteers?
A novel denoising framework combining redundant sensing, IMU data, and adaptive filtering significantly improves the accuracy of wearable PPG and NIBP measurements during motion.
Novel wearables may ease motion artifact limits on SpO2/NIBP monitoring; leaves open clinical validation.
Background and objective Chronic cardiovascular diseases have motivated the development of wearable systems capable of continuously monitoring physiological variables such as oxygen saturation (SpO ₂ 2 ) and non-invasive blood pressure (NIBP). However, motion artifacts remain one of the main limitations affecting the reliability of these measurements in ambulatory environments. This article presents a novel framework for motion artifact reduction in photoplethysmography (PPG) and NIBP signals based on redundant sensing, inertial measurements, ECG-guided temporal synchrony, and multichannel signal processing. Methods The proposed framework combines independent component analysis and recursive least-squares adaptive filtering to separate physiological information from motion-induced interference. Performance was compared with conventional finite impulse response (FIR) filtering and wavelet shrinkage (WS) methods using signal-to-noise ratio (SNR), weighted distortion assessment (WDA), oxygen saturation estimation, and blood pressure estimation metrics. Results The proposed method achieved statistically significant improvements in signal quality and physiological parameter estimation. Average SNR improved from -5.93± 5.05 - 5.93 ± 5.05 dB to -3.43± 2.02 - 3.43 ± 2.02 dB in the red PPG channel ( $$p=0.02$$ p = 0.02 ), while the WDA index increased from 0.48 to 0.73. Oxygen saturation estimation improved from 80.87± 9.09% 80.87 ± 9.09 % in motion-contaminated signals to 94.16± 2.72% 94.16 ± 2.72 % after denoising ( $$p=0.04$$ p = 0.04 ), approaching resting measurements ( 95.23± 2.31% 95.23 ± 2.31 % ). For NIBP signals, statistically significant improvements were also observed ( $$p=0.045$$ p = 0.045 ), particularly for systolic pressure estimation, although diastolic pressure remained affected by residual motion-related distortions. Conclusions The proposed framework improves the robustness of wearable PPG and NIBP monitoring under motion conditions through the combined use of sensor redundancy, ECG-guided temporal coupling, inertial measurements, and adaptive multichannel signal processing. The results demonstrate significant improvements in signal quality and physiological parameter estimation compared with conventional denoising approaches, supporting the potential use of the method in ambulatory cardiovascular monitoring applications.
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Castaño et al. (2026) studied Healthy (n=20). Redundant denoising and adaptive filtering framework vs. Motion-contaminated signals was evaluated on Oxygen saturation estimation (p=0.04). The proposed redundant denoising and adaptive filtering framework significantly improved oxygen saturation estimation from 80.87% in motion-contaminated signals to 94.16%.
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