# Using computer vision and deep learning to measure worker kinematics

> **NIH ALLCDC R21** · UNIVERSITY OF IOWA · 2022 · $196,254

## Abstract

PROJECT SUMMARY/ABSTRACT
Musculoskeletal disorders (MSDs) are among the most frequent and costly nonfatal work-related injuries and
illnesses across virtually all US industry sectors. Responding to the clear need emphasized in the NIOSH
National Research Agenda for Musculoskeletal Health to develop improved methods of estimating exposure to
occupational risk factors for MSDs, this research will validate new software for measuring worker postures and
movements using only standard video as input. The software leverages major advances in computer vision
and machine learning sciences that only recently have enabled measurement of human postures in three
dimensional space using standard two dimensional video or image sources. Ultimately, one of our long-term
goals is to develop applications for occupational safety and health practitioners analogous to widely-used direct
reading instruments for assessing exposure to occupational hazards (e.g., sound pressure meters and gas
monitors). In this initial R21, we propose to validate the postural data our software produces (Aim 1) and
examine agreement between postural information output by our software and that output by more traditional
(but time-consuming) observation-based video analyses (Aim 2). In Aim 1, participants will perform a repetitive,
arm-intensive task involving reaching to and manipulating knobs mounted to a fixture located in front of the
body. We will then estimate the accuracy of neck, shoulder, elbow, wrist, trunk, and knee angular
displacements (i.e., posture over time) measured by our software, compared to data simultaneously collected
using an optical motion capture system. Experimental variables include the range of motion required of
participants to perform the task and the configuration of the camera used to record video of participants during
the task. Results from Aim 1 will provide critical information about the performance of our new software needed
to inform best-practices for implementation in field-capable exposure assessment applications. In Aim 2, we
will reanalyze >1000 workplace videos obtained during the course of a previous prospective study of upper
extremity MSDs among manufacturing workers. Analyses are proposed to assess the inter-method agreement
between automated video analyses (our software) and analyses completed by trained specialist observers
during the course of the prospective study. Results will provide evidence that our software can quantify
occupational exposure to MSD risk factors at a fraction of time needed to perform commonly used observation-
based analyses. The reanalysis of existing workplace videos can also open new pathways to explore
associations between occupational exposures to MSD risk factors and incident health outcomes in future
studies.

## Key facts

- **NIH application ID:** 10493051
- **Project number:** 5R21OH011911-02
- **Recipient organization:** UNIVERSITY OF IOWA
- **Principal Investigator:** Nathan B Fethke
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2022
- **Award amount:** $196,254
- **Award type:** 5
- **Project period:** 2021-09-30 → 2023-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10493051

## Citation

> US National Institutes of Health, RePORTER application 10493051, Using computer vision and deep learning to measure worker kinematics (5R21OH011911-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10493051. Licensed CC0.

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