Baby detector software embedded in digital camera rivals ECG
Non-contact monitoring a step closer for neonatal wards

Date:
August 25, 2021
Source:
University of South Australia
Summary:
Facial recognition is now common in adults, but researchers have
developed software that can reliably detect a premature baby's
face in an incubator and remotely monitor its heart and breathing
rates - rivaling ECG machines and even outperforming them. This is
the first step in using non-contact monitoring in neonatal wards,
avoiding skin tearing and potential infections from adhesive pads.



FULL STORY ========================================================================== University of South Australia researchers have designed a computer vision system that can automatically detect a tiny baby's face in a hospital
bed and remotely monitor its vital signs from a digital camera with the
same accuracy as an electrocardiogram machine.


========================================================================== Using artificial intelligence-based software to detect human faces is
now common with adults, but this is the first time that researchers have developed software to reliably detect a premature baby's face and skin
when covered in tubes, clothing, and undergoing phototherapy.

Engineering researchers and a neonatal critical care specialist from
UniSA remotely monitored heart and respiratory rates of seven infants
in the Neonatal Intensive Care Unit (NICU) at Flinders Medical Centre
in Adelaide, using a digital camera.

"Babies in neonatal intensive care can be extra difficult for computers
to recognise because their faces and bodies are obscured by tubes and
other medical equipment," says UniSA Professor Javaan Chahl, one of the
lead researchers.

"Many premature babies are being treated with phototherapy for jaundice,
so they are under bright blue lights, which also makes it challenging
for computer vision systems." The 'baby detector' was developed using
a dataset of videos of babies in NICU to reliably detect their skin tone
and faces.



========================================================================== Vital sign readings matched those of an electrocardiogram (ECG) and in
some cases appeared to outperform the conventional electrodes, endorsing
the value of non-contact monitoring of pre-term babies in intensive care.

The study is part of an ongoing UniSA project to replace contact-based electrical sensors with non-contact video cameras, avoiding skin
tearing and potential infections that adhesive pads can cause to babies' fragile skin.

Infants were filmed with high-resolution cameras at close range and vital physiological data extracted using advanced signal processing techniques
that can detect subtle colour changes from heartbeats and body movements
not visible to the human eye.

UniSA neonatal critical care specialist Kim Gibson says using neural
networks to detect the faces of babies is a significant breakthrough
for non-contact monitoring.

"In the NICU setting it is very challenging to record clear videos of
premature babies. There are many obstructions, and the lighting can
also vary, so getting accurate results can be difficult. However, the
detection model has performed beyond our expectations.

"Worldwide, more than 10 per cent of babies are born prematurely and
due to their vulnerability, their vital signs need to be monitored continuously.

Traditionally, this has been done with adhesive electrodes placed on the
skin that can be problematic, and we believe non-contact monitoring is
the way forward," Gibson says.

Professor Chahl says the results are particularly relevant given the
COVID-19 pandemic and need for physical distancing.

In 2020, the UniSA team developed world-first technology, now used
in commercial products sold by North American company Draganfly, that
measures adults' vital signs to screen for symptoms of COVID-19.

========================================================================== Story Source: Materials provided by University_of_South_Australia. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Fatema-Tuz-Zohra Khanam, Asanka G. Perera, Ali Al-Naji, Kim Gibson,
Javaan Chahl. Non-Contact Automatic Vital Signs Monitoring
of Infants in a Neonatal Intensive Care Unit Based on
Neural Networks. Journal of Imaging, 2021; 7 (8): 122 DOI:
10.3390/jimaging7080122 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210825113641.htm

--- up 15 weeks, 5 days, 22 hours, 45 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)