Explainable Artificial Intelligence for Activity-Aware Cardiovascular State Estimation from Wearable Signals

Rowan R. Gonzalez; Zhantian He; Akshay R. Gandhi

Authors

Rowan R. Gonzalez Department of Computer Science, Colorado State University, Fort Collins, CO, USA.
Zhantian He Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
Akshay R. Gandhi Department of Computer Science, University of Alabama at Birmingham, Birmingham, AL, USA.

Keywords:

explainable artificial intelligence; wearable sensors; cardiovascular state estimation; photoplethysmography; physical activity; physiological time series; responsible deployment

Abstract

Wearable sensing now enables continuous, non-invasive observation of cardiovascular dynamics through photoplethysmography, electrocardiography, and inertial measurement. However, translating these signals into clinically and behaviorally meaningful cardiovascular state estimates under free-living conditions remains challenging because physical activity introduces nonstationary artifacts, alters autonomic context, and shifts sensor coupling. This paper presents a systems-oriented analysis of explainable artificial intelligence for activity-aware cardiovascular state estimation from wearable signals. It examines how signal processing, representation learning, temporal modeling, and explanation layers can be integrated into an infrastructure that is simultaneously accurate, interpretable, and robust outside controlled environments. Rather than promoting a single model, the paper considers structural trade-offs among gradient-boosted ensembles, recurrent and attention-based deep networks, and interpretable parameterized models. It argues that explanation in wearable cardiovascular monitoring must address multiple audiences, including clinicians, patients, developers, and regulators, and must account for activity context as a first-class element rather than a nuisance variable. The discussion covers motion artifact recovery, adaptive filtering, feature attribution, causal and social dimensions of explanation, fairness across demographic and physiological subgroups, deployment governance, energy constraints, privacy, and long-term sustainability. Case illustrations and cross-domain comparisons highlight how explanation methods developed for computer vision or static tabular data require careful adaptation for streaming physiological time series. The paper concludes that explainability should be designed as a systemic property of the monitoring infrastructure, not appended as a post hoc visualization, and that activity-aware cardiovascular state estimation will benefit from tighter integration between sensing hardware, physiological priors, and transparent machine learning workflows.

References

1. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785

2. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30, 4765–4774.

3. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. https://doi.org/10.1145/2939672.2939778

4. Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, 618–626. https://doi.org/10.1109/ICCV.2017.74

5. 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

6. Ballinger, B., Hsieh, J., Singh, A., Sohoni, N., Wang, J., Tison, G. H., Marcus, G. M., Sanchez, J. M., Maguire, C., Olgin, J. E., & Pletcher, M. J. (2018). DeepHeart: Semi-supervised sequence learning for cardiovascular risk prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1), 2079–2086. https://doi.org/10.1609/aaai.v32i1.11891

7. Zhang, Y., & Yang, Q. (2021). A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering, 34(12), 5586–5609. https://doi.org/10.1109/TKDE.2021.3070203

8. Zheng, X., Dwyer, V. M., Barrett, L. A., Derakhshani, M., & Hu, S. (2022). Adaptive notch-filtration to effectively recover photoplethysmographic signals during physical activity. Biomedical Signal Processing and Control, 72, 103303.

9. 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

10. Temko, A. (2017). Accurate heart rate monitoring during physical exercises using PPG. IEEE Transactions on Biomedical Engineering, 64(9), 2016–2024.

11. Castaneda, D., Esparza, A., Ghamari, M., Soltanpur, C., & Nazeran, H. (2018). A review on wearable photoplethysmography sensors and their potential future applications in health care. International Journal of Biosensors & Bioelectronics, 4(4), 195–202.

12. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607

13. Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. https://doi.org/10.1016/j.artint.2018.07.007

14. Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), e1312. https://doi.org/10.1002/widm.1312

15. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x

16. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems 30, 5998–6008.

17. 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

18. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

19. Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S., & Goldenberg, A. (2019). Do no harm: A roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337–1340. https://doi.org/10.1038/s41591-019-0548-6

20. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2

Explainable Artificial Intelligence for Activity-Aware Cardiovascular State Estimation from Wearable Signals

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