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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.\n <\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The proposed method achieved statistically significant improvements in signal quality and physiological parameter estimation. Average SNR improved from\n \n \n $$-5.93\\pm 5.05$$<\/jats:tex-math>\n \n \n -<\/mml:mo>\n 5.93<\/mml:mn>\n \u00b1<\/mml:mo>\n 5.05<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n dB to\n \n \n $$-3.43\\pm 2.02$$<\/jats:tex-math>\n \n \n -<\/mml:mo>\n 3.43<\/mml:mn>\n \u00b1<\/mml:mo>\n 2.02<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n dB in the red PPG channel (\n \n \n $$p=0.02$$<\/jats:tex-math>\n \n \n p<\/mml:mi>\n =<\/mml:mo>\n 0.02<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ), while the WDA index increased from 0.48 to 0.73. Oxygen saturation estimation improved from\n \n \n $$80.87\\pm 9.09\\%$$<\/jats:tex-math>\n \n \n 80.87<\/mml:mn>\n \u00b1<\/mml:mo>\n 9.09<\/mml:mn>\n %<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n in motion-contaminated signals to\n \n \n $$94.16\\pm 2.72\\%$$<\/jats:tex-math>\n \n \n 94.16<\/mml:mn>\n \u00b1<\/mml:mo>\n 2.72<\/mml:mn>\n %<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n after denoising (\n \n \n $$p=0.04$$<\/jats:tex-math>\n \n \n p<\/mml:mi>\n =<\/mml:mo>\n 0.04<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ), approaching resting measurements (\n \n \n $$95.23\\pm 2.31\\%$$<\/jats:tex-math>\n \n \n 95.23<\/mml:mn>\n \u00b1<\/mml:mo>\n 2.31<\/mml:mn>\n %<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ). For NIBP signals, statistically significant improvements were also observed (\n \n \n $$p=0.045$$<\/jats:tex-math>\n \n \n p<\/mml:mi>\n =<\/mml:mo>\n 0.045<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ), particularly for systolic pressure estimation, although diastolic pressure remained affected by residual motion-related distortions.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.1007\/s13239-026-00850-0","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T19:38:54Z","timestamp":1785785934000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reduction of Motion Artifacts in Cardiorespiratory Vital Signs as Oxygen Saturation and Non-Invasive Blood Pressure Through Redundant Denoising and Adaptive Filtering Methods for Wearable Healthcare Monitoring Systems"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8014-4773","authenticated-orcid":false,"given":"Fabian Andres","family":"Casta\u00f1o","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1132-5794","authenticated-orcid":false,"given":"Alher Mauricio","family":"Hern\u00e1ndez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"850_CR1","unstructured":"World Health Organization, World Health Statistics 2025: Monitoring Health for the SDGs, Sustainable Development Goals, World Health Organization, Geneva, 2025. https:\/\/iris.who.int\/publications\/b\/78420, licence: CC BY-NC-SA 3.0 IGO."},{"key":"850_CR2","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/1878-5085-5-6","volume":"5","author":"O Golubnitschaja","year":"2014","unstructured":"Golubnitschaja O, Kinkorova J, Costigliola V (2014) Predictive, Preventive and Personalised Medicine as the hardcore of \u2018Horizon 2020\u2019: EPMA position paper. 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