Preliminary Validation of a Low-Cost Motion Analysis System Based on RGB Cameras to Support the Evaluation of Postural Risk Assessment

Featured Application: We introduce a motion capture tool that uses at least one RGB-camera, exploiting an open-source deep learning model with low computational requirements, already used to im-plement mobile apps for mobility analysis. Experimental results suggest the suitability of this tool to perform posture analysis aimed at assessing the RULA score, in a more efficient way.This paper introduces a low-cost and low computational marker-less motion capture system based on the acquisition of frame images through standard RGB cameras. It exploits the open-source deep learning model CMU, from the tf-pose-estimation project. Its numerical accuracy and its usefulness for ergonomic assessment are evaluated by a proper experiment, designed and performed to: (1) compare the data provided by it with those collected from a motion capture golden standard system; (2) compare the RULA scores obtained with data provided by it with those obtained with data provided by the Vicon Nexus system and those estimated through video analysis, by a team of three expert ergonomists. Tests have been conducted in standardized laboratory conditions and involved a total of six subjects. Results suggest that the proposed system can predict angles with good consistency and give evidence about the tool's usefulness for ergonomist.

Automated summary

This paper introduces a low-cost and low computational marker-less motion capture system based on the acquisition of frame images through standard RGB cameras. Experimental results suggest that the proposed system can predict angles with good consistency and provide useful evidence for ergonomists.