Abstract
Recent years have seen an increase in interest in remote photoplethysmography (rPPG), as it is a fast, inexpensive, and convenient method for contactless estimation of a person’s heart rate from facial videos and has potential in cardiac monitoring. Compared to traditional photoplethysmography (PPG), rPPG offers ease of access for disadvantaged and vulnerable members of the population, as
this method saves cost and time by reducing frequent visits to the hospital. However, there are currently limitations to using rPPG in practice due to issues with consistent response skin colour, subject movement, and lighting artefacts. In this work we develop a new framework, ChPOS, by combining two traditional algorithms, CHROM and plane orthogonal to skin (POS). We modified the POS algorithm by incorporating the additional feature of the chrominance colour signal and changing the projection axis, to improve the accuracy of heart rate detection on subjects with darker skin complexion. The performance of our model is validated on two publicly available datasets, UBFC-RPPG and PURE. We compare the approach with state-of-the-art algorithms and the results show that our algorithm outperforms state of art models in the estimation of heart rate, with a mean absolute error (MAE) of 5.71 and root mean squared error (RMSE) of 7.27 on the UBFC-RPPG database and MAE of 5.39 and RMSE of 6.61 on the PURE
database.
this method saves cost and time by reducing frequent visits to the hospital. However, there are currently limitations to using rPPG in practice due to issues with consistent response skin colour, subject movement, and lighting artefacts. In this work we develop a new framework, ChPOS, by combining two traditional algorithms, CHROM and plane orthogonal to skin (POS). We modified the POS algorithm by incorporating the additional feature of the chrominance colour signal and changing the projection axis, to improve the accuracy of heart rate detection on subjects with darker skin complexion. The performance of our model is validated on two publicly available datasets, UBFC-RPPG and PURE. We compare the approach with state-of-the-art algorithms and the results show that our algorithm outperforms state of art models in the estimation of heart rate, with a mean absolute error (MAE) of 5.71 and root mean squared error (RMSE) of 7.27 on the UBFC-RPPG database and MAE of 5.39 and RMSE of 6.61 on the PURE
database.
| Original language | English |
|---|---|
| Title of host publication | 2023 34th Irish Signals and Systems Conference (ISSC) |
| Publisher | IEEE |
| Pages | 1-6 |
| Number of pages | 6 |
| ISBN (Electronic) | 9798350340570 |
| ISBN (Print) | 9798350340587 |
| DOIs | |
| Publication status | Published online - 3 Jul 2023 |
| Event | 34th Irish Signals and Systems Conference (ISSC 2023) - University College Dublin, Dublin, Ireland Duration: 13 Jun 2023 → 14 Jun 2023 https://issc.ie/index.html |
Publication series
| Name | 2023 34th Irish Signals and Systems Conference (ISSC) |
|---|---|
| Publisher | IEEE Control Society |
Conference
| Conference | 34th Irish Signals and Systems Conference (ISSC 2023) |
|---|---|
| Abbreviated title | ISSC 2023 |
| Country/Territory | Ireland |
| City | Dublin |
| Period | 13/06/23 → 14/06/23 |
| Internet address |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- Remote Plethysmography (rPPG)
- RGB Face Video
- Heart Rate
- Chrominance Colour Signal
- Plane Orthogonal to Skin