Researchers have developed a method that uses the camera on a person's smartphone or computer to take their pulse and respiration signal from a real-time video of their face.

The development comes at a time when telehealth has become a critical way for doctors to provide health care while minimizing in-person contact during Covid-19.

The researchers presented the system in December at the Neural Information Processing Systems conference.

Now the team is proposing a better system to measure these physiological signals.

This system is less likely to be tripped up by different cameras, lighting conditions or facial features, such as skin colour, according to the researchers who will present these findings on 8 April at the Association for Computing Machinery (ACM) Conference on Health, Interference, and Learning.

"Every person is different," said lead study author Xin Liu, a UW doctoral student.

"So this system needs to be able to quickly adapt to each person's unique physiological signature, and separate this from other variations, such as what they look like and what environment they are in."

The system then used spatial and temporal information from the videos to calculate both vital signs.

While the system worked well on some datasets, it still struggled with others that contained different people, backgrounds and lighting. This is a common problem known as "overfitting," the team said.

The researchers improved the system by having it produce a personalized machine learning model for each individual.

From there, it can focus on that area and measure the pulse and respiration rate.

While this new system outperforms its predecessor when given more challenging datasets, especially for people with darker skin tones, there is still more work to do, the team said.

(This story was published from a syndicated feed. Only the headline and picture has been edited by FIT).