# Automatic Multimodal Assessment of Pain in Dementia

> **NIH NIH R01** · CARNEGIE-MELLON UNIVERSITY · 2021 · $344,726

## Abstract

Project Summary/Abstract
Chronic and acute pain conditions increase with aging and with age-associated conditions, including Alzheimer's
disease and similar dementias, causing suffering, exacerbating diminished quality of life, and likely leading to
further morbidity and increased mortality. Understanding and treatment of pain is particularly challenging
among populations whose ability to communicate verbally may be impaired, among which patients with
Alzheimer's disease and related dementias are of particular importance if only because of the increased aging
population. A substantial body of research suggests that methods based on nonverbal indicators of pain yield
reliable and valid measurement of pain in patient with dementias. Some of this work has indicated that patients
with dementia may in fact be hyper-reactive to pain, rendering the pursuit of improved techniques for assessing
pain in the dementia population even more important. Existing techniques for assessing nonverbal indicators of
pain have several disadvantages which limit their utility. In particular, they require human observers, are
dependent on specialty training, can be laborious, and cannot be applied on a continuous basis. Advances in
computer vision and machine learning have the potential to overcome some of these shortcomings. Our “parent
proposal” project aims to develop advanced, automated computer-vision and machine-learning models for
assessing nonverbal aspects of pain in a longitudinal cohort study of people with low back pain. This supplement
proposal seeks to extend our work on the “parent grant”, which aims to “build a fully automatic, multimodal
(face, head, and body movement) system to measure the occurrence and intensity of low back pain from video”.
In this supplement project, these aims will be extended to older adults with dementia. We will refine the
principles discovered for pain assessment in the low back pain population, extend, and evaluate their application
in a sample of older adults with dementias in extended-care facilities.
Participants, long-term care residents with dementia, were video-recorded during a baseline state as they were
lying still on a bed or examination table and then as they underwent a standardized protocol of movements
designed to identify painful areas. Participants' face, head, and body movement will be used for the development
of automatic measures of the occurrence and intensity of pain. To do so, face, head, and body movement will be
automatically tracked using fully- automatic methods. The tracking results will be used to train end-to-end deep-
leaning based classifiers to automatically measure the occurrence and intensity of pain in older adults with
dementia. To investigate the validity of the proposed classifiers, we will compare automated measurement of
pain intensity to reliable and objective pain intensity coding. MANOVA will be used to quantify the relationship
between the individual modalities and their combination f...

## Key facts

- **NIH application ID:** 10288413
- **Project number:** 3R01NR018451-02S1
- **Recipient organization:** CARNEGIE-MELLON UNIVERSITY
- **Principal Investigator:** Zakia Hammal
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $344,726
- **Award type:** 3
- **Project period:** 2020-03-16 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10288413

## Citation

> US National Institutes of Health, RePORTER application 10288413, Automatic Multimodal Assessment of Pain in Dementia (3R01NR018451-02S1). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10288413. Licensed CC0.

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