# SCH: AI-empowered wearable multimodal sensors (AI-MEDALLION) for noninvasive monitoring

> **NIH NIH R01** · OREGON HEALTH & SCIENCE UNIVERSITY · 2024 · $318,710

## Abstract

Over the last decade, significant progress has been made in wearable sensors that detect biomarkers in a
continuous and non-invasive manner from biofluids such as sweat. Parkinson's disease (PD) is the
 second-most common neurodegenerative disorder in the United States, and early diagnosis and
management of the disease course of PD remains an urgent task. Among all biomarker types, the sweat
 metabolites, peptides, and anions can reflect both the phenotypic states of cells or organs and their
 dynamic responses to external stimuli, such as drug treatment. However, significant technical challenges
exist in developing sweat metabolic biomarkers and wearable sweat sensors in PD monitoring: 1) lack of
 known biomarkers and accurate whole metabolomic and peptide profiles in PD patients' sweat; 2) the
 selected biomarkers need to be optimized for PD system and sensor technology; 3) current wearable
sweat sensors fall short in sensitivity. This project aims to fill these gaps by proposing a novel framework
for predicting and optimizing the PD metabolic biomarkers using large-scale multi-omics data, to innovate
 non-invasive wearable sweat sensor with high sensitivity and specificity. Specifically, the biomarker
 panels and sweat sensor will be developed by bridging state-of-the-art AI and nanotechnologies to assist
the diagnosis and monitoring of PD, and will be validated clinically. The proposed research will provide an
 integrative infrastructure for developing novel PD biomarkers and non-invasive personal wearable
 devices. We will develop computational methods to solve a set of unaddressed questions in biomarker
 discovery, namely identification of PD-specific functional variations, prediction of metabolites and peptides
 in body fluids, and optimization of biomarker selection for personal wearable sensor (Aim 1). We will gain
 fundamental knowledge and technical capabilities in designing, processing, and optimizing of 2D
materials-based wearable sensors for versatile applications. A physics-based, data-driven protocol will be
 developed to reveal the fundamental process-structure-property-performance relation for wearable
sensors (Aim 2). We will develop new biomarker sets and wearable sweat sensors and test the
performance in assisting diagnosis and monitoring of PD, which could be extended to other
biological/disease systems (Aim 3). This proposed study will fill the gaps of disease-specific biomarker
 prediction and 2D material optimization in developing a clinically applicable non-invasive wearable device.

## Key facts

- **NIH application ID:** 11063327
- **Project number:** 1R01LM014720-01
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Wenzhuo Wu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $318,710
- **Award type:** 1
- **Project period:** 2024-09-18 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11063327

## Citation

> US National Institutes of Health, RePORTER application 11063327, SCH: AI-empowered wearable multimodal sensors (AI-MEDALLION) for noninvasive monitoring (1R01LM014720-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/11063327. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*