# MESH: Multimodal Estimators for Sensing Health

> **NIH NIH R35** · NEW YORK UNIVERSITY · 2024 · $365,168

## Abstract

Project Summary/Abstract
The PI’s goal is to develop an interdisciplinary research program and a foundational algorithmic
framework for reliably inferring health states from physiological signals acquired using wearable
and portable physiological monitoring devices. Uncovering the health states will unleash an array
of applications related to monitoring inflammation, metabolism, fatigue and interoceptive
awareness. For instance, it is well known that hormones play an important role in maintaining
homeostasis of the body, while cytokines are crucial as mediators of immune response after
surgery or infection that disturbs this homeostasis. Adverse external influences such as stress
can profoundly alter the hormone or cytokine production in patients, affecting their health and
complicating recovery from diseases or surgery. The knowledge of their secretion and modulation
in response to major influences such as cardiac surgery, medications, disease, and stress is
crucial to the health of patients, more so when more than one of these factors is concurrently
present. Thus, there is a compelling but unfulfilled need to quantify hidden health states of
inflammation, metabolism, fatigue and interoceptive awareness. The PI’s laboratory seeks to
pioneer system-theoretic computational toolsets for understanding the pulsatile signaling
underlying the physiological signals (e.g., cytokines, hormones, eye movement) related to
different health states and capturing the unobserved temporal dynamics of one’s health states in
a biologically plausible manner while considering extensive experimental settings and clinical
data. This project will determine the pulsatile physiological signaling from discrete, noisy
measurements by performing signal deconvolution to extract the neuronal stimuli underlying their
modulation, and will build decoders to quantify internal health states that are indicative of
inflammation, metabolism, fatigue and interoceptive awareness using both unlabeled information
as well as labels via feedback from patients and clinicians, to help physicians interpret
physiological data and inform patient-specific treatment in a holistic manner. The proposed
research will use de-identified data both from publicly available datasets and those collected by
the PI’s collaborators (e.g., endocrinology, rheumatology, neurosurgery, psychiatry,
neuroscience) using wearable or portable devices to perform signal analysis and compare the
results against previously published results, known experimental settings, and clinical knowledge
to validate the models and provide new insight.

## Key facts

- **NIH application ID:** 10912653
- **Project number:** 5R35GM151353-02
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Rose Faghih
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $365,168
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10912653

## Citation

> US National Institutes of Health, RePORTER application 10912653, MESH: Multimodal Estimators for Sensing Health (5R35GM151353-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10912653. Licensed CC0.

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