ErgoReport: A Holistic Posture Assessment Framework Based on Inertial Data and Deep Learning
Diogo R. Martins, Sara M. Cerqueira, Ana Pombeiro, Alexandre Ferreira da Silva, Ana Maria A. C. Rocha, Cristina P. Santos

TL;DR
ErgoReport is a new framework using inertial data and deep learning to assess posture risk and generate intuitive reports for ergonomic improvement.
Contribution
A novel posture assessment framework combining inertial data and deep learning to generate actionable ergonomic reports.
Findings
ErgoReport provides a GUI-based report linking ergonomic scores to specific postures.
The framework considers previous postures' impact on joint stress using a kinematic wear model.
Three ergonomists found the report useful for improving assessment effectiveness and ease of use.
Abstract
Awkward postures are a significant contributor to work-related musculoskeletal disorders (WRMSDs), which represent great social and economic burdens. Various posture assessment tools assess WRMSD risk but fall short in providing an elucidating risk breakdown to expedite the typical time-consuming ergonomic assessments. Quantifying, automating, but also complementing posture risk assessment become crucial. Thus, we developed a framework for a holistic posture assessment, able to, through inertial data, quantify the ergonomic risk and also qualitatively identify the posture leading to it, using Deep Learning. This innovatively enabled the generation of a report in a graphical user interface (GUI), where the ergonomic score is intuitively associated with the postures adopted, empowering workers to learn which are the riskiest postures, and helping ergonomists and managers to redesign…
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Taxonomy
TopicsErgonomics and Musculoskeletal Disorders · Hand Gesture Recognition Systems
