Wednesday, 25 August 2021 11:44

Researchers develop computer vision system that can detect babies’ faces while monitoring their vital signs

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University of South Australia researchers have developed 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.

UniSA researchers say that while artificial intelligence-based software can detect adult human faces, this is the first time they have developed a software reliably to detect a premature baby’s face and skin when covered in tubes, clothing, and undergoing phototherapy.

UniSA engineering researchers and neonatal critical care specialist collaborated to monitor the 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,” explains UniSA and lead researcher professor Javaan Chahl.

“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,” Chahl adds.

The baby detector was developed using a dataset of videos of babies in Nicu to reliably detect their skin tone and faces.

Researchers say the vital sign readings are in par with an electrocardiogram and in some cases, it appeared to outperform the conventional electrodes, endorsing the value of non-contact monitoring of pre-term babies in intensive care.

According to UniSA, the study is part of an ongoing project to replace contact-based electrical sensors with non-contact video cameras to avoid skin tearing and potential infections that can damage 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 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,” Gibson says.

“Worldwide, more than 10% 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 concludes 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.

The results have been published in the Journal of Imaging.

Image: Peter K Burian via Wikimedia Commons (CC BY-SA 4.0)


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Kenn Anthony Mendoza

Kenn Anthony Mendoza is the newest member of the iTWire team. Kenn is also a contributing writer for South China Morning Post Style, and has written stories on Korean entertainment, Asian and European royalty, Millionaires and Billionaires, and LGBTQIA+ issues. He has been published in Philippine newspapers, magazines, and online sites: Tatler PhilippinesManila BulletinCNN Philippines LifePhilippine StarManila Times, and The Daily Tribune. Kenn now covers all aspects of technology news for iTWire.com.

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