A Systematic Literature Review of the Factors that Influence the Accuracy of Consumer Wearable Health Device Data
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Red LEDs are more commonly used in medical settings, are not absorbed as well by the skin allowing light to transmit deeper and detect multiple biological measures, but have a lower signal-to-noise ratio and are more susceptible to motion artifacts.
“In contrast, red LEDs, which are more commonly used in medical settings, are not absorbed as well by the skin allowing the light to transmit deeper and allowing for detection of multiple biological measures, but it has a lower signal-to-noise ratio and is more susceptible to motion artifacts23.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [23]
Motion artifacts and level of physical activity are two related factors identified as influencing wearable heart rate accuracy.
“two related factors identified as influencing wearable HR accuracy are motion artifacts23 and level of physical activity46.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [23]
Wearables that use red light will likely be more effective on darker skin tones because red light is not absorbed by melanin, which may allow for more accurate heart rate measurements.
“wearables that use red light will likely be more effective on darker skin tones as it is not absorbed by melanin, which may allow for more accurate HR measurements23.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [23]
Wearables that use green PPG or a combination of green and red PPG are likely to perform better during movement and physical activity.
“indicating that wearables that use green PPG or a combination of green and red PPG are likely to perform better during movement and physical activity23.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [23]
Wearable manufacturers use proprietary algorithms to translate PPG signals to heart rate measurements, which may influence accuracy.
“Wearable manufacturers use proprietary algorithms to translate PPG signals to HR measurements, which may influence accuracy23.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [23]
PPG green light is less susceptible to motion artifacts, whereas PPG red light is more susceptible to motion artifacts.
“one strength of PPG green light is that it is less susceptible to motion artifacts, whereas PPG red light is more susceptible to motion artifacts”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [23]
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Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research
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- Authors
- Mahloko, L., & Adebesin, F.
- Affiliations
- University of Pretoria (ZA)
- Corresponding author
- Adebesin, Funmi
- ORCID
- Adebesin, Funmi
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Volume
- LNCS-12067
- Issue
- Part II
- Pages
- 96-107
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- PMC
- PMC7134235
- OpenAlex
- W3016016217
- Cited by
- 23
- References
- 47
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 4 · 2022: 8 · 2021: 5 · 2020: 3
- FWCI
- 9.08
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed Central (publishedVersion)
- HAL (submittedVersion, cc-by)
- UpSpace Institutional Repository (University of Pretoria) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- data accuracy, heart rate measurement, physical activity monitoring, sleep monitoring, fitness tracking, sensor types, tracking algorithm, energy consumption, processing capacity, developing countries
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