Using computer vision and deep learning to measure worker kinematics

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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.