Long-term health monitoring of adults, babies and small children without concern for skin injury or allergic reactions is the aim of a project at Georgia Tech, which has produced a stretchy patch that can broadcast electrocardiogram (ECG), heart rate, respiratory rate and motion activity data.
Connections are to gold skin-like electrodes through printed conductors that can stretch with the medical film in which they are embedded.
“This health monitor has a key advantage for young children who are always moving, since the soft conformal device can accommodate that activity with a gentle integration onto the skin,” said Georgia Tech biomedical engineer Woon-Hong Yeo. “This is designed to meet the electronic health monitoring needs of people whose sensitive skin may be harmed by conventional monitors.”
“When you put a conventional electrode on the chest, movement from sitting up or walking creates motion artifacts that are challenging to separate from the signals you want to measure,” said Yeo. “Because our device is soft and conformal, it moves with the skin and provides information that cannot be seen with the motion artifacts of conventional sensors.”
Overall, it is 75mm in diameter – although a smaller version is in the pipeline – and uses three electrodes to pick-up signals.
Thin-film mesh-like copper tracks provide connections through the soft substrate, and a strain-isolated soft substrate interfaces flexible parts with the inflexible chip assembly.
Two versions have been developed: The soft elastomer medical film version, approved for use in wound care and can be used for up to two weeks, and one based on medical tape and designed for short-term use in hospitals.
“The devices are completely dry and do not require a gel to pick up signals from the skin,” Yeo explained. “There is nothing between the skin and the ultrathin sensor, so it is comfortable to wear. The membrane is waterproof, so an adult could take a shower while wearing it.”
Long-term connection means long-term recording of ECG data, opening the door to AI analysis.
“We use deep learning to monitor the signals while comparing them to data from a larger group of patients,” Yeo said.
As well as a smaller version, the team is planning to add monitoring for temperature, blood oxygen and blood pressure.