Robust heart rate estimation using wrist-based PPG signals in the presence of intense physical activities
Abstract
Heart rate tracking from a wrist-type photoplethysmogram (PPG) signal during intensive physical activities is a challenge that is attracting more attention thanks to the introduction of wrist-worn wearable computers. Commonly-used motion artifact rejection methods coupled with simple periodicity-based heart rate estimation techniques are incapable of achieving satisfactory heart rate tracking performance during intense activities. In this paper, we propose a two-stage solution. Firstly, we introduce an improved spectral subtraction method to reject the spectral components of motion artifacts. Secondly, instead of using heuristic mechanisms, we formalize the spectral peaks selection process as the shortest path search problem and validate its effectiveness. Analysis on the experimental results based on a published database shows that: (1) Our proposed method outperforms three other comparable methods with regards to heart rate estimation error. (2) The proposed method is a promising candidate for both offline cardiac health analysis and online heart rate tracking in daily life, even during intensive physical motions.
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Heart rate measurements from wearable devices are often less accurate during physical activity or cyclic wrist motions.
“Several studies have demonstrated that HR measurements from wearable devices are often less accurate during physical activity or cyclic wrist motions.”
From Investigating sources of inaccuracy in wearable optical heart rate sensors (2020) · cites this paper as [38]
Wearable devices have higher error during activity than at rest.
“Our analyses reinforces the growing body of research demonstrating that wearable devices have higher error during activity than at rest.8,11,35,38,39”
From Investigating sources of inaccuracy in wearable optical heart rate sensors (2020) · cites this paper as [38]
All these motions range between 0.1 and 20 Hz, which is within the frequency range for heart rate (1-4 Hz).
“All these motions range between 0.1 and 20 Hz, which is within the frequency range for heart rate (1–4 Hz) [163].”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [163]
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- Authors
- Zong, C., & Jafari, R.
- Affiliations
- The University of Texas at Dallas (US)
- Venue
- 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2015-08
- Volume
- 2015
- Pages
- 8078-8082
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2294523693
- Cited by
- 39
- References
- 13
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 5 · 2023: 3 · 2022: 2 · 2021: 7 · 2020: 2 · 2019: 5 · 2018: 4 · 2017: 5 · 2016: 3 · 2015: 1
- FWCI
- 8.34
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- heart rate estimation, photoplethysmography, wrist-worn sensors, motion artifact rejection, spectral subtraction, shortest path search, wearable devices, physical activity, cardiac health monitoring
- MeSH terms
- Algorithms, Heart Rate, Humans, Signal Processing, Computer-Assisted, Wrist, Exercise, Artifacts, Photoplethysmography
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