A robust heart rate monitoring scheme using photoplethysmographic signals corrupted by intense motion artifacts
Abstract
GOAL: Although photoplethysmographic (PPG) signals can monitor heart rate (HR) quite conveniently in hospital environments, trying to incorporate them during fitness programs poses a great challenge, since in these cases, the signals are heavily corrupted by motion artifacts. METHODS: In this paper, we present a novel signal processing framework which utilizes two channel PPG signals and estimates HR in two stages. The first stage eliminates any chances of a runaway error by resorting to an absolute criterion condition based on ensemble empirical mode decomposition. This stage enables the algorithm to depend very little on the previously estimated HR values and to discard the need of an initial resting phase. The second stage, on the other hand, increases the algorithm's robustness against offtrack errors by using recursive least squares filters complemented with an additional novel technique, namely time-domain extraction. RESULTS: Using this framework, an average absolute error of 1.02 beat per minute (BPM) and standard deviation of 1.79 BPM are recorded for 12 subjects performing a run with peak velocities reaching as high as 15 km/h. CONCLUSION: The performance of this algorithm is found to be better than the other recently reported algorithms in this field such as TROIKA and JOSS. SIGNIFICANCE: This method is expected to greatly facilitate the presently available wearable gadgets in HR computation during various physical activities.
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Secondary sensors can be integrated into PPG sensors to detect motion, including accelerometers, gyroscopes, piezoelectric transducers, or another wavelength of light as a motion reference.
“As implemented by consumer-grade HR monitors, secondary sensors can also be integrated into PPG sensors to detect motion. These include accelerometers [175,176,177,178,179], gyroscopes [179,180], piezoelectric transducers [181], or the utilization of another wavelength of light as a motion reference [182,183].”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [176]
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- Authors
- Khan, E., Al Hossain, F., Uddin, S. Z., Alam, S. K., & Hasan, M. K.
- Affiliations
- Bangladesh University of Engineering and Technology (BD)
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2016-03
- Volume
- 63
- Issue
- 3
- Pages
- 550-562
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2288945889
- Cited by
- 153
- References
- 20
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 9 · 2023: 16 · 2022: 14 · 2021: 20 · 2020: 15 · 2019: 29 · 2018: 11 · 2017: 21 · 2016: 8 · 2015: 1
- FWCI
- 6.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Biomedical Engineering (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 monitoring, photoplethysmography, motion artifacts, ensemble empirical mode decomposition, recursive least squares, wearable sensors, physical activity
- MeSH terms
- Adolescent, Adult, Algorithms, Female, Heart Rate, Humans, Male, Middle Aged, Signal Processing, Computer-Assisted, Artifacts, Photoplethysmography, Young Adult
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