# Autonomous Pain Recognition in Non-Verbal and Critically Ill Patients

> **NIH NIH R21** · UNIVERSITY OF FLORIDA · 2022 · $142,341

## Abstract

The under-assessment of pain response is one of the primary barriers to the adequate treatment of pain in
critically ill patients, and is associated with many negative outcomes. Nonetheless, many ICU patients are
unable to self-report their pain intensity. Currently, behavioral pain scales are used to assess pain in nonverbal
patients. Unfortunately, these scales require repetitive manual administration by overburdened nurses, and
show variability in pain intensity ratings by nurses. Furthermore, manual pain assessment tools cannot monitor
pain continuously and autonomously. The PIs’ long-term goal is to specify pain intensity in an autonomous and
precise manner. The overall objective of this application is to build the foundation of an autonomous, clinically-
available pain assessment system by developing and validating pain recognition algorithms in a fully
uncontrolled ICU setting. The central hypothesis is we can autonomously assess facial pain expressions and
patient activity. The rationale is that autonomous pain quantification can reduce nurse workload and can
enable real-time pain monitoring. Contextualization of pain with respect to patient function can also lead to
improved functional and clinical outcomes. The overall objective will be achieved by pursuing two specific
aims. (1) Developing and validating a pervasive sensing system in two 24-beds ICUs (ICU) to determine if
deep learning algorithms can accurately assess pain facial expressions from image data, when compared to
existing assessment tools. (2) Developing and validating algorithms for pain contextualization using
autonomous activity recognition. The approach is innovative, because it departs from status quo by
autonomously assessing pain and functional status in the ICU. The proposed research is significant since it will
address several key problems and barriers in critical care, including manual repetitive ICU assessments and
lack of granular and continuous pain and function measures. Ultimately, the results are expected to improve
patient outcomes and decrease hospitalization costs, as well as lifelong complications.

## Key facts

- **NIH application ID:** 10075272
- **Project number:** 5R21EB027344-03
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Parisa Rashidi
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $142,341
- **Award type:** 5
- **Project period:** 2019-02-01 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10075272

## Citation

> US National Institutes of Health, RePORTER application 10075272, Autonomous Pain Recognition in Non-Verbal and Critically Ill Patients (5R21EB027344-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10075272. Licensed CC0.

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