Rose Faghih wins National Institutes of Health’s Maximizing Investigators' Research Award for research into tracking people’s invisible health conditions with wearable devices
In a groundbreaking multi-year research project led by Rose Faghih, associate professor of biomedical engineering at NYU Tandon School of Engineering, portable devices are poised to unveil hidden dimensions of people's health, potentially improving medical diagnoses and broadening healthcare access.
Faghih has been awarded the prestigious Maximizing Investigators' Research Award (MIRA) for Early Stage Investigators by the National Institutes of Health, securing a $1,825,840 five-year grant to fuel her project known as "MESH: Multimodal Estimators for Sensing Health."
MESH's primary objective is to provide insights into four pivotal health indicators: inflammation, metabolism, fatigue, and interoceptive awareness. Achieving this goal hinges on the development of sophisticated algorithms by Faghih's team, capable of interpreting physiological data collected from portable devices individuals carry or wear.
“MESH is a highly multidisciplinary project, bringing together collaborators from general medicine, nursing, neurosurgery, neuroscience, rheumatology, neuroendocrinology, psychiatry, and learning sciences. We want healthcare providers to be able to easily monitor people’s hidden health states, so they can catch potentially serious underlying medical conditions early,” said Faghih, who is the Principal Investigator (PI) of this single PI project. “Relatively inexpensive and portable devices of the future have the great potential to eventually provide dynamic diagnostic health information that is usually available only in medical settings and at single points in time. Ultimately MESH gets us one step closer to the possibility of better diagnosis for everyone and more accessible healthcare for populations often underserved by conventional healthcare structures.”
To train algorithms that detect inflammation, the MESH team will use data about cytokines — a protein related to inflammation — collected from patients during and after cardiac surgery. For the metabolism algorithm, they will leverage hormonal data from women undergoing obesity treatment. For fatigue, they will use hormonal data from women with chronic fatigue syndrome and fibromyalgia.
For its algorithm to decipher interoceptive awareness — the ability to perceive and recognize internal bodily sensations and feelings that are associated with certain mental health and neurodegenerative disorders — the MESH team will draw data from eye-tracking experiments.
“There is a great potential that MESH algorithms eventually get integrated into future biosensors to enable individuals to wear compact devices akin to Continuous Glucose Monitors, continually monitoring their bodies' biochemical signals to track their health states,” said Faghih. “These devices would feed data into MESH's algorithms, tracking inflammation, metabolism, and fatigue. Smartphones could serve as tracking hubs for eye movements, crucial for MESH’s interoceptive awareness algorithm.”
Faghih’s pioneering research using wearable devices to understand people’s cognitive arousal has earned her professional acclaim. Her ongoing MINDWATCH research develops algorithms that reveal people’s cognitive arousal states by interpreting their skins’ electrodermal activity. The MINDWATCH work garnered her a CAREER Award from the National Science Foundation and a spot on MIT Technology Review’s Innovators Under 35 in 2020.