Evaluating the Accuracy of AI-Powered Ergonomic Assessments Using a Commercial Computer Vision System

Abstract

Workers performing material handling tasks are at high risk of work-related musculoskeletal disorders (WMSDs). While AI-based computer vision tools claim to assess ergonomic risks with minimal input, their accuracy remains uncertain. This study evaluated a commercial AI system’s ability to estimate key parameters of the Revised NIOSH Lifting Equation (RNLE) by comparing its outputs to those from a marker-based motion capture system. Ten participants completed lifting tasks while being recorded by three cameras and motion capture sensors. The AI-analyzed video outputs were compared to ground truth data. Results showed significant inaccuracies in the AI’s estimates—especially for horizontal and vertical distances—leading to overestimated Recommended Weight Limits and underestimated Lifting Index values. Among the camera views, the side view produced the most accurate results, while the moving camera performed worst. These findings highlight the need for improvement in commercial AI tools before they can be reliably used in ergonomic risk assessments.