The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks
Open accessAbstract
Photoplethysmography (PPG)-based continuous heart rate monitoring is essential in a number of domains, e.g., for healthcare or fitness applications. Recently, methods based on time-frequency spectra emerged to address the challenges of motion artefact compensation. However, existing approaches are highly parametrised and optimised for specific scenarios of small, public datasets. We address this fragmentation by contributing research into the robustness and generalisation capabilities of PPG-based heart rate estimation approaches. First, we introduce a novel large-scale dataset (called PPG-DaLiA), including a wide range of activities performed under close to real-life conditions. Second, we extend a state-of-the-art algorithm, significantly improving its performance on several datasets. Third, we introduce deep learning to this domain, and investigate various convolutional neural network architectures. Our end-to-end learning approach takes the time-frequency spectra of synchronised PPG- and accelerometer-signals as input, and provides the estimated heart rate as output. Finally, we compare the novel deep learning approach to classical methods, performing evaluation on four public datasets. We show that on large datasets the deep learning model significantly outperforms other methods: The mean absolute error could be reduced by 31 % on the new dataset PPG-DaLiA, and by 21 % on the dataset WESAD.
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Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection
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Cited by
Photoplethysmogram Analysis and Applications: An Integrative Review
Park, J., Seok, H. S., Kim, S.-S., & Shin, H. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2021.808451 · Cited by 422
Wearable Photoplethysmography for Cardiovascular Monitoring
Charlton, P. H., Kyriacou, P. A., Mant, J., Marozas, V., Chowienczyk, P., & Alastruey, J. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3149785 · Cited by 250
A Review of Wearable Multi-Wavelength Photoplethysmography
Ray, D., Collins, T., Woolley, S., & Ponnapalli, P. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3121476 · Cited by 155
Karolcik, S., Manginas, V., Chanh, H. Q., Daniels, J., Giang, N. T., Huyen, V. N. T., Hoang, M. T. V., Phan Nguyen Quoc, K., Hernandez, B., Ming, D. K., et al. (2024) · eBioMedicine · doi:10.1016/j.ebiom.2024.105164 · Cited by 18
A PPG Signal Dataset Collected in Semi-Naturalistic Settings Using Galaxy Watch
Park, S., Zheng, D., & Lee, U. (2025) · Scientific Data · doi:10.1038/s41597-025-05152-z · Cited by 12
Metadata
- Authors
- Reiss, A., Indlekofer, I., Schmidt, P., & Van Laerhoven, K.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-07-12
- Volume
- 19
- Issue
- 14
- Pages
- 3079
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2962182608
- Cited by
- 377
- References
- 47
- FWCI
- 10.61
- Type
- journal article
- Language
- en
- Open access
- gold
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