Bio-Signal Sensing Improves Continuous Monitoring for Better Health Outcomes
By Leah Scott
LESA faculty researcher Mona Hella, PI of the Rensselaer Integrated Circuits & Systems (RICS) Laboratory is developing a bio-signal sensing chip-platform to process digitized output from data collected from patients with readout capabilities that will increase the signal to noise ratio for continuous monitoring applications. Continuous monitoring of the body allows medical experts and researchers alike to get a better idea of what’s happening inside the body. Chronic disrupted sleep/wake cycles from brain injuries, epilepsy, heart arrhythmias, and high blood pressure are among the most common disorders requiring around-the-clock, remote medical monitoring.
Monitoring organ and other human system functions accurately can be very invasive, expensive, and uncomfortable requiring wearing an uncomfortable or clunky apparatus. Brain monitoring, for instance, may require intracranial implants. The signals Dr. Hella is detecting, however, are transmitted directly from muscles, the brain, heart, and blood pressure through noninvasive, biosensor applications embedded in a patch comprised of semiconductor chips embedded on a flexible substrate.
The sensing platform is very sophisticated, able to extract very weak signals that can be digitized and transmitted from the wearer’s body to a digital-ready device such as a cell phone. Sensors for processing applications for sleep pattern recognition and brain activity are part of a larger body of ongoing research at LESA but Dr. Hella’s approach is to sense the body’s energy signals from a wearable patch no matter how weak while simultaneously utilizing the body’s own energy transmitted through the skin rather than over the airwaves, eliminates both the need for a radio receiver or a bulky battery affixed to a rigid substrate. The goal is for the body itself becomes the transmitter of both the data and the energy needed to powers the chips across a flexible substratist while saving power and energy through ubiquitous connectivity.
Additional Sources
Biometric Data, Algorithms To Unlock Key Information About Circadian Clock
Automatic Sleep Estimation Using Actigraphy