Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review
Open accessAbstract
Non-invasive photoplethysmography (PPG) technology was developed to track heart rate during motion. Automated analysis of PPG has made it useful in both clinical and non-clinical applications. However, PPG-based heart rate tracking is a challenging problem due to motion artifacts (MAs) which are main contributors towards signal degradation as they mask the location of heart rate peak in the spectra. A practical analysis system must have good performance in MA removal as well as in tracking. In this article, we have presented state-of-art techniques in both areas of the automated analysis, i.e., MA removal and heart rate tracking, and have concluded that adaptive filtering and multi-resolution decomposition techniques are better for MA removal and machine learning-based approaches are future perspective of heart rate tracking. Hence, future systems will be composed of machine learning-based trackers fed with either empirically decomposed signal or from output of adaptive filter.
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PPG sensors may have limitations, such as being bulky to use in daily life (e.g. ECG sensors in a chest strap), inaccurate measurement under motions (e.g. PPG sensors), sensitivity to environmental conditions, high power consumption, etc.
“However, they may still have their own limitations, such as being bulky to use in daily life (e.g. ECG sensors in a chest strap), inaccurate measurement under motions (e.g. PPG sensors), sensitivity to environmental conditions, high power consumption, etc. [170].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [170]
Motion artifacts are a primary cause of bad signal quality.
“Motion artifacts are a primary cause of bad signal quality, and research has suggested various methods for mitigating these26,39,40.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [40]
BVP data is dependent on measuring reflected light from a light-emitting diode (LED), and this signal can be heavily skewed by contamination from external light sources and motion artifacts, sometimes to the point of being irrecoverable through retrospective data processing.
“BVP data is dependent on measuring reflected light from a light-emitting diode (LED), and this signal can be heavily skewed by contamination from external light sources and motion artifacts, sometimes to the point of being irrecoverable through retrospective data processing40,57,58.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [40]
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- Authors
- Ismail, S., Akram, U., & Siddiqi, I.
- Affiliations
- Bahria University (PK)
- National University of Sciences and Technology (PK)
- Corresponding author
- Ismail, Shahid
- Venue
- EURASIP Journal on Advances in Signal Processing
- Published
- 2021-01-11
- Volume
- 2021
- Issue
- 1
- ISSN
- 1110-8657 (ISSN-L)
- 1687-6180
- OpenAlex
- W3127423251
- Cited by
- 101
- References
- 106
- Citations by year
- 2026: 20 · 2025: 15 · 2024: 21 · 2023: 21 · 2022: 13 · 2021: 11
- FWCI
- 5.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- EURASIP Journal on Advances in Signal Processing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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
- photoplethysmography, heart rate monitoring, motion artifacts, motion artifact removal, adaptive filtering, multiresolution decomposition, non-invasive physiological monitoring
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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