Privacy-Preserving Industrial Ergonomics: mmWave-Based Automated REBA Scoring and Pose Estimation

2026-07-01

AI Summary

This study addresses the inefficiency of manual REBA assessments in industrial settings and the privacy concerns associated with vision-based approaches by proposing, for the first time, an end-to-end multitask learning framework leveraging millimeter-wave radar to enable privacy-preserving automatic REBA scoring through 3D human skeletal reconstruction. The method integrates biomechanical constraints and temporal smoothness losses, and employs an oversampling strategy to mitigate data imbalance in high-risk postures. Evaluated on the MMFi dataset, the model achieves a REBA risk-level classification accuracy of 77.78%, with a mean absolute error of 0.93 for high-risk samples and an inference latency of only 5.70 milliseconds.

Abstract

Work-related Musculoskeletal Disorders (WMSDs) require continuous ergonomic assessments. While Rapid Entire Body Assessment (REBA) is a gold-standard observation tool, manual monitoring is labor-intensive, and vision-based automation leads to privacy concerns. This paper proposes a novel end-to-end multi-task learning framework for privacy-preserving ergonomic assessment using millimetre-wave (mmWave) radar. A spatio-temporal backbone reconstructs 3D human skeletons, which serves as the biomechanical foundation for a subsequent regression head to generate REBA risk scores. To overcome the sparsity of radar point clouds, we utilise a multi-objective loss function incorporating biomechanical limits and temporal smoothness constraints. Furthermore, we implement an oversampling strategy to address the imbalance of high-risk postures in existing datasets. Experimental results on MMFi dataset demonstrate that our framework achieves a Categorical Accuracy of 77.78% and real-time performance with an inference latency of 5.70 ms. Our method reaches a High-risk REBA MAE of 0.93, which significantly outperforms both direct regression and two-stage pipelines in high-risk scenarios, providing a robust solution for non-invasive industrial ergonomic assessment.

Problem

• Privacy-Preserving
• Industrial Ergonomics
• REBA Scoring
• Work-related Musculoskeletal Disorders
• mmWave Radar

Innovation

• mmWave radar
• REBA scoring
• privacy-preserving ergonomics
• multi-task learning
• 3D pose estimation