First Fabric-Based Sensor Enables Real-Time Monitoring of Physiological Signals

By HospiMedica International staff writers
Posted on 01 Feb 2023

Clothing fitted with tiny electromechanical sensors for remote detection of disease or physiological issues can continuously monitor the body’s movements and vital signs over long periods of time. However, the application of increased pressure such as when receiving a hug or taking a nap lying on the stomach overwhelms the sensor, interrupting the flow of data and making the sensor useless for monitoring. Now, a team of researchers have developed an all-fabric pressure sensor that works even when the pressure is pushing down on the wearer.

Researchers at the University of Massachusetts Amherst (Amherst, MA, USA) have synthesized a new material that resolves the problem of pressure, paving the way for wearable, unobtrusive sensitive sensors. The sensor remains operational even when hugged, sat upon, leaned on or squished by daily interactions. This is made possible by vapor-printing clothing fabrics with piezoionic materials like PEDOT-Cl (p-doped poly (3,4-ethylenedioxythiophene-chloride). With this method, even the smallest body movement, for instance a heartbeat, leads to the ions being redistributed throughout the sensor. Simply put, the fabric converts the body’s mechanical motion into an electrical signal, which can then be monitored.


Image: The new sensor makes use of PEDOT-Cl-coated cotton sandwiched between electrodes (Photo courtesy of UMass Amherst)

“Imagine comfortable clothing that would monitor your body’s movements and vital signs continuously, over long periods of time,” said Trisha L. Andrew, professor of chemistry and chemical engineering at the University of Massachusetts Amherst, who led the team. “Such clothing would give clinicians fine-grained details for remote detection of disease or physiological issues.”

Zohreh Homayounfar, lead author of the study and a graduate student at UMass Amherst, stated that “this is the first fabric-based sensor allowing for real-time monitoring of sensitive target populations, from workers laboring in stressful industrial settings, to kids and rehabilitation patients.”

Related Links:
University of Massachusetts Amherst 


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