Motion Artifact Suppression in Photoplethysmography Using Adaptive Neural Networks
Keywords:
photoplethysmography; motion artifacts; adaptive neural networks; wearable systems; health monitoring; socio-technical infrastructureAbstract
Photoplethysmography has become a foundational sensing modality in wearable and clinical monitoring because it enables continuous estimation of cardiovascular and respiratory parameters through noninvasive optical interrogation of peripheral tissue. However, the clinical utility and real-world reliability of photoplethysmographic signals are severely limited by motion artifacts arising from physical activity, sensor displacement, and environmental vibration. Conventional filtering and signal decomposition methods offer partial mitigation, but they often fail under nonstationary motion and complex physiological variability. This paper presents a system-level analysis of adaptive neural network approaches for motion artifact suppression in photoplethysmography. Rather than concentrating solely on algorithmic novelty, the paper examines the architectural trade-offs between convolutional, recurrent, and encoder-decoder models, the infrastructure requirements for edge and cloud deployment, data governance and fairness considerations, and the long-term sustainability of learning-based physiological monitoring systems. The discussion integrates perspectives from wearable computing, biomedical signal processing, machine learning, and healthcare policy to demonstrate that motion artifact suppression should be treated not as an isolated signal processing problem but as a socio-technical challenge requiring robust data pipelines, representative training data, transparent validation, and regulatory alignment. The analysis further considers how adaptive neural models can be embedded into resource-constrained devices while maintaining clinical reliability and equitable performance across diverse patient populations.
References
1. Allen, J. (2007). Photoplethysmography and its application in clinical physiological measurement. Physiological Measurement, 28(3), R1–R39. https://doi.org/10.1088/0967-3334/28/3/R01
2. Elgendi, M. (2012). On the analysis of fingertip photoplethysmogram signals. Current Cardiology Reviews, 8(1), 14–25. https://doi.org/10.2174/157340312801215782
3. Asada, H. H., Shaltis, P., Reisner, A., Rhee, S., & Hutchinson, R. C. (2003). Mobile monitoring with wearable photoplethysmographic biosensors. IEEE Engineering in Medicine and Biology Magazine, 22(3), 28–40. https://doi.org/10.1109/MEMB.2003.1213624
4. Zhang, Z., Pi, Z., & Liu, B. (2015). TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Transactions on Biomedical Engineering, 62(2), 522–531. https://doi.org/10.1109/TBME.2014.2359372
5. Temko, A. (2017). Accurate heart rate monitoring during physical exercises using PPG. IEEE Transactions on Biomedical Engineering, 64(9), 2015–2024. https://doi.org/10.1109/TBME.2017.2676243
6. Reiss, A., Indlekofer, I., Schmidt, P., & Van Laerhoven, K. (2019). Deep PPG: Large-scale heart rate estimation with convolutional neural networks. Sensors, 19(14), 3079. https://doi.org/10.3390/s19143079
7. Biswas, D., Everson, L., Liu, M., Panwar, M., Van Helleputte, N., & Van Hoof, C. (2019). CorNET: Deep learning framework for PPG-based heart rate estimation and biometric identification. IEEE Journal of Biomedical and Health Informatics, 23(3), 1014–1022. https://doi.org/10.1109/JBHI.2018.2872010
8. Salehizadeh, S. M. A., Dao, D., Bolkhovsky, J., Cho, C., Mendelson, Y., & Chon, K. H. (2016). A novel time-varying spectral filtering algorithm for reconstruction of motion artifact corrupted heart rate signals. IEEE Transactions on Biomedical Engineering, 63(5), 955–966. https://doi.org/10.1109/TBME.2015.2480132
9. Jarchi, D., & Casson, A. J. (2017). Towards photoplethysmography-based estimation of instantaneous heart rate during physical activity. IEEE Transactions on Biomedical Engineering, 64(10), 2324–2334. https://doi.org/10.1109/TBME.2017.2665063
10. Charlton, P. H., Birrenkott, D. A., Pimentel, M. A. F., Johnson, A. E. W., Alastruey, J., Ward, S., ... & Kyriacou, P. A. (2018). Breathing rate estimation from the electrocardiogram and photoplethysmogram: A review. IEEE Reviews in Biomedical Engineering, 11, 2–20. https://doi.org/10.1109/RBME.2017.2763681
11. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
12. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (pp. 234–241). Springer. https://doi.org/10.1007/978-3-319-24574-4_28
13. Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65–69. https://doi.org/10.1038/s41591-018-0268-3
14. Vullings, R., de Vries, B., & Bergmans, J. W. M. (2011). An adaptive Kalman filter for ECG signal enhancement. IEEE Transactions on Biomedical Engineering, 58(4), 1094–1103. https://doi.org/10.1109/TBME.2010.2099226
15. Majumder, S., Mondal, T., & Deen, M. J. (2017). Wearable sensors for remote health monitoring. Sensors, 17(1), 130. https://doi.org/10.3390/s17010130
16. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
17. Chen, W., Jaques, N., Taylor, S., Sano, A., Fedor, S., & Picard, R. W. (2015). Wavelet-based motion artifact removal for electrodermal activity. In 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) (pp. 1–6). IEEE. https://doi.org/10.1109/BSN.2015.7299391
18. Zheng, X., Dwyer, V. M., Barrett, L. A., Derakhshani, M., & Hu, S. (2023). Rapid vital sign extraction for real-time opto-physiological monitoring at varying physical activity intensity levels. IEEE Journal of Biomedical and Health Informatics, 27(7), 3107-3118.
19. Liang, Y., Elgendi, M., Chen, Z., & Ward, R. (2018). An optimal filter for short photoplethysmogram signals. Scientific Data, 5, 180076. https://doi.org/10.1038/sdata.2018.76
20. Naeini, E. K., Azimi, I., Rahmani, A. M., Liljeberg, P., & Dutt, N. (2020). A real-time PPG quality assessment approach for IoT-based smart health. IEEE Transactions on Biomedical Circuits and Systems, 14(5), 1184–1196. https://doi.org/10.1109/TBCAS.2020.3013845
21. Heikenfeld, J., Jajack, A., Rogers, J., Gutruf, P., Tian, L., Pan, T., Li, R., ... & Wang, J. (2018). Wearable sensors: Modalities, challenges, and prospects. Lab on a Chip, 18(2), 217–248. https://doi.org/10.1039/C7LC00914C
22. Vollmer, M., Bläsing, D., & Kaderali, L. (2020). Alignment of multi-sensor data for robust estimation of respiratory rate from wrist-worn devices. IEEE Journal of Biomedical and Health Informatics, 24(11), 3161–3171. https://doi.org/10.1109/JBHI.2020.2984741
23. Poh, M. Z., McDuff, D. J., & Picard, R. W. (2010). Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics Express, 18(10), 10762–10774. https://doi.org/10.1364/OE.18.010762
24. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679
25. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Artificial Intelligence and Machine Learning Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.