# Continuous Monitoring of COVID-19 Symptomatology for Elderly Patients in Long Term Care Facilities Using Advanced, Soft, and Flexible Sensors Mounted on the Suprasternal Notch

> **NIH NIH R41** · SONICA, LLC · 2020 · $249,304

## Abstract

PROJECT SUMMARY: COVID-19 is significantly more lethal in the elderly1 with the
greatest risk in those cared for in long-term care facilities (LTCs) where mortality rates
range from 19% to 72% worldwide. Monitoring COVID-19 infections in LTCs remains a
particular challenging. The existing and a continued expected shortage of sufficient
molecular COVID-19 testing coupled to false negative rates as high as 15% necessitates
a critical need for new and complementary technologies that can surveil, alert, and track
COVID-19 infections in this population. Our group are pioneers in the development of
novel soft electronics. Our recent publication, supported by our active Phase I STTR,
was published in Nature Biomedical Engineering detailing a next generation ultra-low
profile, soft, and flexible sensor (ADAM) that continuously measures subtle acousto-
mechanic signals generated by the body via an embedded high-frequency, 3-axis
accelerometer in direct mechanical communication with the skin. The ADAM sensor
communicates via Bluetooth with our custom mobile application for real time streaming
as well as on sensor data storage enabling stand-alone operation. All data streams are
cloud synchronized (HIPAA compliant). The highly novel soft, flexible nature allows for
the ADAM sensor to be mountable on unusual locations of high information density.
Specifically, we exploit the SN—the only location on the body where there is no
dampening effect at the skin level with the intrathoracic cavity. This enables a SN-
mounted ADAM sensor to capture heart rate (HR), respiratory rate (RR), temperature,
physical activity (PA), swallow count, and talk time, along with additional novel
respiratory biomarkers relevant to COVID-19. In this proposal, we propose to develop a
new COVID-19 software package, machine learning enhancements to our cough
algorithm, and validation in LTCs with both elderly patients and staff to evaluate
usability, feasibility, and adherence. The high level of technology readiness with partner
LTCs allows us to deploy efficiently to generate essential data for a future FDA
Emergency Use Authorization. Our team of experts in engineering, dermatology,
gerontology, and machine learning are highly qualified to develop this COVID-19
surveillance system that offers both commercial and clinical value with broad
applicability to a wide range of other respiratory and chronic medical conditions after the
pandemic subsides.

## Key facts

- **NIH application ID:** 10167884
- **Project number:** 3R41AG062023-02S1
- **Recipient organization:** SONICA, LLC
- **Principal Investigator:** Shuai Xu
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $249,304
- **Award type:** 3
- **Project period:** 2018-09-30 → 2021-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10167884

## Citation

> US National Institutes of Health, RePORTER application 10167884, Continuous Monitoring of COVID-19 Symptomatology for Elderly Patients in Long Term Care Facilities Using Advanced, Soft, and Flexible Sensors Mounted on the Suprasternal Notch (3R41AG062023-02S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10167884. Licensed CC0.

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