A Multi-Wavelength Opto-Electronic Patch Sensor to Effectively Detect Physiological Changes against Human Skin Types
Open accessAbstract
Different skin pigments among various ethnic group people have an impact on spectrometric illumination on skin surface. To effectively capture photoplethysmographic (PPG) signals, a multi-wavelength opto-electronic patch sensor (OEPS) together with a schematic architecture of electronics were developed to overcome the drawback of present PPG sensor. To perform a better in vivo physiological measurement against skin pigments, optimal illuminations in OEPS, whose wavelength is compatible with a specific skin type, were optimized to capture a reliable physiological sign of heart rate (HR). A protocol was designed to investigate an impact of five skin types in compliance with Von Luschan’s chromatic scale. Thirty-three healthy male subjects between the ages of 18 and 41 were involved in the protocol implemented by means of the OEPS system. The results show that there is no significant difference (p: 0.09, F = 3.0) in five group tests with the skin types across various activities throughout a series of consistent measurements. The outcome of the present study demonstrates that the OEPS, with its multi-wavelength illumination characteristics, could open a path in multiple applications of different ethnic groups with cost-effective health monitoring.
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Inaccurate PPG heart rate measurements occur up to 15% more frequently in dark skin than in light skin.
“previous research demonstrated that inaccurate PPG HR measurements occur up to 15% more frequently in dark skin as compared to light skin”
From Investigating sources of inaccuracy in wearable optical heart rate sensors (2020) · cites this paper as [31]
The Introduction says skin pigmentation can affect PPG signal quality and mentions anecdotal errors of 3–5% in black skin. It does not report that inaccurate PPG heart-rate measurements occur 15% more frequently in dark skin than in light skin. The Discussion reports no significant effect of skin type on heart-rate measurements with the sensor tested (p = 0.09). (LLM, conf=0.87)
Experimental — accuracy not yet measuredDarker skin contains more melanin, which absorbs more green light than lighter skin.
“likely because darker skin contains more melanin which absorbs more green light than lighter skin.”
From Investigating sources of inaccuracy in wearable optical heart rate sensors (2020) · cites this paper as [31]
The Introduction (page unavailable) links darker skin with higher melanin levels and says melanin attenuates incident light. It does not establish that darker skin absorbs more green light than lighter skin; its green-light discussion concerns heart-rate detection, not a direct absorption comparison. (LLM, conf=0.83)
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Cites
The validity and practicality of sun-reactive skin types I through VI
Fitzpatrick, T. B. (1988) · Archives of Dermatology · doi:10.1001/archderm.1988.01670060015008 · Cited by 4,339 · see citing works
Anderson, R. R., & Parrish, J. A. (1981) · Journal of Investigative Dermatology · doi:10.1111/1523-1747.ep12479191 · Cited by 2,456 · see citing works
Human skin pigmentation: Melanocytes modulate skin color in response to stress
Costin, G.-E., & Hearing, V. J. (2007) · The FASEB Journal · doi:10.1096/fj.06-6649rev · Cited by 976 · see citing works
Zonios, G., Bykowski, J., & Kollias, N. (2001) · Journal of Investigative Dermatology · doi:10.1046/j.0022-202x.2001.01577.x · Cited by 477 · see citing works
Fitzpatrick, T. (1975) · Medical Entomology and Zoology · Cited by 365 · see citing works
Jubran, A., & Tobin, M. J. (1990) · Chest · doi:10.1378/chest.97.6.1420 · Cited by 296 · see citing works
Ralston, A., Webb, R. K., & Runciman, W. B. (1991) · Anaesthesia · doi:10.1111/j.1365-2044.1991.tb11501.x · Cited by 190 · see citing works
Computer simulation of the skin reflectance spectra
Meglinski, I. V., & Matcher, S. J. (2002) · Computer Methods and Programs in Biomedicine · doi:10.1016/s0169-2607(02)00099-8 · Cited by 179 · see citing works
Influence of skin type and wavelength on light wave reflectance
Fallow, B. A., Tarumi, T., & Tanaka, H. (2013) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-013-9436-7 · Cited by 162 · see citing works
Spīgulis, J., Gailite, L., Lihachev, A., & Erts, R. (2007) · Applied Optics · doi:10.1364/ao.46.001754 · Cited by 98 · see citing works
Weinschenk, S. W., Beise, R. D., & Lorenz, J. (2016) · European Journal of Applied Physiology · doi:10.1007/s00421-016-3401-3 · Cited by 56 · see citing works
Skin pigmentation as an influence on the accuracy of pulse oximetry.
Emery, J. R. (1987) · PubMed · Cited by 29 · see citing works
Smartphone-Based Photoplethysmogram Measurement
Kurylyak, Y., Lamonaca, F., & Grimaldi, D. (2022) · River Publishers eBooks · doi:10.1201/9781003337911-5 · Cited by 28 · see citing works
Evaluation of video magnification for nonintrusive heart rate measurement
Sarkar, A., Abbott, A. L., Doerzaph, Z. R., & Sykes, K. (2016) · IEEE International Conference on Control, Measurement and Instrumentation · doi:10.1109/cmi.2016.7413797 · Cited by 8 · see citing works
Spīgulis, J., Gailite, L., & Lihachev, A. (2007) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.724071 · Cited by 6 · see citing works
Alzahrani, A. (2016) · Loughborough University Institutional Repository (Loughborough University) · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
1 of the 17 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [12]Lim H.W. Soter N.A. Clinical Photomedicine CRC Press New York, NY, USA 1993
Cited by
Sorted by citations
Investigating sources of inaccuracy in wearable optical heart rate sensors
Bent, B., Goldstein, B. A., Kibbe, W. A., & Dunn, J. P. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0226-6 · Cited by 673 · see citing works
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 450 · see citing works
Kim, K. B., & Baek, H. J. (2023) · Electronics · doi:10.3390/electronics12132923 · Cited by 238 · see citing works
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 · see citing works
Limiting racial disparities and bias for wearable devices in health science research
Colvonen, P. J., DeYoung, P. N., Bosompra, N.-O. A., & Owens, R. L. (2020) · Sleep · doi:10.1093/sleep/zsaa159 · Cited by 137 · see citing works
Cabanas, A. M., Fuentes-Guajardo, M., Latorre, K., León, D., & Martín-Escudero, P. (2022) · Sensors · doi:10.3390/s22093402 · Cited by 124 · see citing works
Scardulla, F., Cosoli, G., Spinsante, S., Poli, A., Iadarola, G., Pernice, R., Busacca, A., Pasta, S., Scalise, L., & D’Acquisto, L. (2023) · Measurement · doi:10.1016/j.measurement.2023.113150 · Cited by 120 · see citing works
Ajmal, Boonya-Ananta, T., Rodriguez, A. J., Du Le, V. N., & Ramella-Roman, J. C. (2021) · Biomedical Optics Express · doi:10.1364/boe.439893 · Cited by 79 · see citing works
Scardulla, F., D’Acquisto, L., Colombarini, R., Hu, S., Pasta, S., & Bellavia, D. (2020) · Sensors · doi:10.3390/s20185052 · Cited by 72 · see citing works
Self-citationAccuracy and Precision of Wearable Devices for Real-Time Monitoring of Swimming Athletes
Cosoli, G., Antognoli, L., Veroli, V., & Scalise, L. (2022) · Sensors · doi:10.3390/s22134726 · Cited by 69 · see citing works
Oliveira, R., Ferreira, J. C. M., Azevedo, L. F., & Almeida, I. F. (2023) · Cosmetics · doi:10.3390/cosmetics10010014 · Cited by 52 · see citing works
Optimizing a novel PPG sensor patch via optical simulations towards accurate heart rates
Pribadi, E. F., Pandey, R. K., & Chao, P. C.-P. (2020) · Microsystem Technologies · doi:10.1007/s00542-020-04895-6 · Cited by 34 · see citing works
MW-PPG Sensor: An on-Chip Spectrometer Approach
Chang, C.-C., Wu, C.-T., Choi, B. I., & Fang, T.-J. (2019) · Sensors · doi:10.3390/s19173698 · Cited by 30 · see citing works
The validation of smartphone applications for heart rate measurement
Pipitprapat, W., Harnchoowong, S., Suchonwanit, P., & Sriphrapradang, C. (2018) · Annals of Medicine · doi:10.1080/07853890.2018.1531144 · Cited by 24 · see citing works
Alharbi, S., Hu, S., Mulvaney, D. J., Barrett, L. A., Yan, L., Blanos, P., Elsahar, Y., & Adema, S. (2018) · Sensors · doi:10.3390/s19010118 · Cited by 24 · see citing works
Self-citationSkin tone, confidence, and data quality of heart rate sensing in WearOS smartwatches
Ray, I., Liaqat, D., Gabel, M., & de Lara, E. (2021) · 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) · doi:10.1109/percomworkshops51409.2021.9431120 · Cited by 23 · see citing works
Wang, C.-F., Wang, T.-Y., Kuo, P.-H., Wang, H., Li, S.-Z., Lin, C., Chan, S.-C., Liu, T., Lo, Y., Lin, S.-H., et al. (2023) · Biosensors · doi:10.3390/bios13030321 · Cited by 24 · see citing works
Carek, A. M., Jung, H., & Inan, O. T. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2960063 · Cited by 18 · see citing works
Zheng, X., Dwyer, V. M., Barrett, L. A., Derakhshani, M., & Hu, S. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3268240 · Cited by 16 · see citing works
Self-citationDetermining factors of pulse oximetry accuracy: a literature review
Valladares, D. L., Mateu, L. A. B., Cortés-Carmona, N., Fuentes-Lizana, G., Cabanas, A. M., Latorre-Progulakis, K., Fuentes‐Guajardo, M., & Martín-Escudero, P. (2024) · Revista Clínica Española (English Edition) · doi:10.1016/j.rceng.2024.04.005 · Cited by 18 · see citing works
Blood Pressure Prediction by a Smartphone Sensor using Fully Convolutional Networks
Baek, S., Jang, J.-Y., Cho, S. H., Choi, J. M., & Yoon, S. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175902 · Cited by 17 · see citing works
D’Acquisto, L., Scardulla, F., Montinaro, N., Pasta, S., Zangla, D., & Bellavia, D. (2019) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroi4.2019.8792834 · Cited by 14 · see citing works
Bui, N. T., Nguyen, T. M. T., Truong, T. T., Nguyen, B. L.-H., & Nguyen, T. T. (2022) · Biosensors and Bioelectronics X · doi:10.1016/j.biosx.2022.100223 · Cited by 14 · see citing works
Todorov, A. R., Torah, R. N., Wagih, M., Ardern‐Jones, M. R., & Beeby, S. (2023) · Sensors · doi:10.3390/s23083935 · Cited by 15 · see citing works
Blanos, P., Hu, S., Mulvaney, D. J., & Alharbi, S. (2018) · Design and Quality for Biomedical Technologies · doi:10.1117/12.2287854 · Cited by 13 · see citing works
Self-citationScardulla, F., Cosoli, G., Gnoffo, C., Antognoli, L., Bongiorno, F., Diana, G., Scalise, L., D’Acquisto, L., & Arnesano, M. (2025) · Sensors · doi:10.3390/s25144477 · Cited by 12 · see citing works
Factores determinantes de la precisión de la oximetría de pulso: revisión bibliográfica
Valladares, D. L., Mateu, L. A. B., Cortés-Carmona, N., Fuentes-Lizana, G., Cabanas, A. M., Latorre-Progulakis, K., Fuentes‐Guajardo, M., & Martín-Escudero, P. (2024) · Revista Clínica Española · doi:10.1016/j.rce.2024.03.001 · Cited by 11 · see citing works
Pasta, S., Blanos, P., Yan, L., Hu, S., Scardulla, F., D’Acquisto, L., & Barrett, L. A. (2018) · Design and Quality for Biomedical Technologies · doi:10.1117/12.2287845 · Cited by 9 · see citing works
Self-citationTAMURA, T., & Maeda, Y. (2017) · Springer eBooks · doi:10.1007/978-3-319-69362-0_6 · Cited by 7 · see citing works
Racial discrepancies in oximetry: where do we stand?
Knight, M., Subbe, C. P., & Inada-Kim, M. (2021) · Anaesthesia · doi:10.1111/anae.15635 · Cited by 7 · see citing works
Preliminary Analisys on the Effect of Skin Temperature on Photoplethysmographic Signal
Scardulla, F., Riggi, C., Diana, G., & D’Acquisto, L. (2024) · IEEE International Workshop on Metrology for Living Environment (MetroLivEnv) · doi:10.1109/metrolivenv60384.2024.10615655 · Cited by 6 · see citing works
Bachir, W. (2023) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2023.123181 · Cited by 5 · see citing works
Badolato, E., Little, A., & Le, V. N. D. (2024) · Optics Express · doi:10.1364/oe.510977 · Cited by 5 · see citing works
Cabanas, A. M., Martín-Escudero, P., Pagán, J., & Mery, D. (2025) · British Journal of Anaesthesia · doi:10.1016/j.bja.2025.02.014 · Cited by 6 · see citing works
Владимирович, Д. В., Golubova, N., Potapova, E., & Dunaev, A. (2021) · Journal of Biomedical Photonics & Engineering · doi:10.18287/jbpe21.07.040306 · Cited by 5 · see citing works
Signal Quality in Continuous Transcutaneous Bilirubinometry
Crivellaro, F., Costa, A., & Vieira, P. M. (2024) · Sensors · doi:10.3390/s24186154 · Cited by 4 · see citing works
Blood pressure acquisitions with a prototypal PPG-based device
Scardulla, F., Montinaro, N., & D’Acquisto, L. (2022) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot54413.2022.9831598 · Cited by 4 · see citing works
Scardulla, F., Cosoli, G., Antognoli, L., Gnoffo, C., Diana, G., Bongiorno, F., Scalise, L., D’Acquisto, L., & Arnesano, M. (2025) · IEEE International Workshop on Metrology for Living Environment (MetroLivEnv) · doi:10.1109/metrolivenv64961.2025.11106958 · Cited by 2 · see citing works
Devaki, V., Lakshmanan, M. K., & Jayanthi, T. (2023) · International Conference on Artificial Intelligence and Signal Processing (AISP) · doi:10.1109/aisp57993.2023.10134818 · Cited by 1 · see citing works
Investigating Sources of Inaccuracy in Wearable Optical Heart Rate Sensors (Preprint)Preprint
Bent, B., Goldstein, B. A., Kibbe, W. A., & Dunn, J. (2020) · doi:10.2196/preprints.18126
- Authors
- Yan, L., Hu, S., Alzahrani, A., Alharbi, S., & Blanos, P.
- Affiliations
- Shanghai University (CN)
- Loughborough University (GB)
- Corresponding author
- Hu, Sijung
- Venue
- Biosensors
- Publisher
- MDPI AG
- Published
- 2017-06-21
- Volume
- 7
- Issue
- 2
- Pages
- 22
- ISSN
- 2079-6374 (ISSN-L)
- PMC
- PMC5487964
- OpenAlex
- W2675618241
- Cited by
- 40
- References
- 17
- Citations by year
- 2025: 3 · 2024: 5 · 2023: 8 · 2022: 5 · 2021: 5 · 2020: 6 · 2019: 3 · 2018: 4 · 2017: 1
- FWCI
- 1.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Biosensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Loughborough University Institutional Repository (Loughborough University) (submittedVersion, cc-by)
- Loughborough University Research Repository (Loughborough University) (submittedVersion, cc-by)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- photoplethysmography, multi-wavelength illumination, skin pigmentation, skin type, heart rate monitoring, physiological signal detection, wearable sensors, health monitoring, ethnic groups
- MeSH terms
- Adolescent, Adult, Humans, Male, Pigments, Biological, Signal Processing, Computer-Assisted, Skin, Skin Pigmentation, Biosensing Techniques, Photoplethysmography, Optics and Photonics
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