# Noncontact Remote Monitoring for the Detection of Opioid-Induced Respiratory Depression

> **NIH NIH R43** · AUTONOMOUS HEALTHCARE, INC. · 2023 · $325,001

## Abstract

The US opioid crisis continues to have a catastrophic impact on human lives and the ongoing COVID-19
pandemic is compounding its effects. Based on the statistics published by the CDC, 91,799 drug overdose
deaths occurred in the US in 2020, where the age-adjusted overdose deaths increased by 31% from 2019 to
2020. In addition, opioids, which cause respiratory depression, were involved in 75% of all drug overdose
deaths in the US. We propose to build on our work in non-invasive monitoring of vital signs to develop an FDA-
regulated medical device with a primary application in monitoring patients for opioid-induced respiratory
depression. This includes at-home monitoring of patients with chronic pain being treated with high-dose opioid
prescription medications or patients suffering from opioid use disorder (OUD) as well as monitoring subjects
with OUD at supervised injection sites (also known as supervised consumption spaces). Our overall goal is to
develop a non-contact multi-modal monitoring system for the detection of opioid-induced respiratory
depression at home and in supervised injection sites. While radar is capable of penetrating through clothing
and blankets to measure chest wall movements resulting from respiration, it requires the guidance of depth
imaging to target a person and the chest area. Our specific aims are: 1. Estimate tidal volume using a
noncontact monitoring system. Our current technology is capable of detecting respiratory rate with a high
degree of accuracy for stationary subjects. However, robust detection of respiratory depression involves
monitoring of respiratory rate, pattern, and depth (i.e., tidal volume). As part of this specific aim, we will develop
a framework to estimate tidal volume of a stationary subject using radar and depth information, where we
estimate tidal volume from chest wall displacements. Furthermore, we will extract features to characterize
respiratory pattern from the acquired radar signal. As a primary validation of this estimation framework, our
system will be tested on 20 healthy volunteers. The outcome of the test will provide us with preliminary data
regarding the accuracy of the radar and the depth-based tidal volume estimation as compared with the gold
standard. 2. Develop and validate a framework for integrating data from sensors to detect respiratory
depression. In this specific aim, we will develop a framework to use the respiratory rate, respiratory pattern,
and tidal volume information from the radar and depth camera to determine if respiratory depression has
occurred. This involves a two-step approach, where we extract respiratory features to characterize respiratory
patterns to complement respiratory rate and tidal volume, and then use a machine learning model to detect the
occurrence of respiratory depression. To help with design the right model, we will collect data using our radar
and depth imaging system from anesthetized pigs going through opioid-induced respiratory depression.

## Key facts

- **NIH application ID:** 10684530
- **Project number:** 1R43DA058470-01
- **Recipient organization:** AUTONOMOUS HEALTHCARE, INC.
- **Principal Investigator:** Behnood Gholami
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $325,001
- **Award type:** 1
- **Project period:** 2023-06-15 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10684530

## Citation

> US National Institutes of Health, RePORTER application 10684530, Noncontact Remote Monitoring for the Detection of Opioid-Induced Respiratory Depression (1R43DA058470-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10684530. Licensed CC0.

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