用毫米波雷达实现无感工位姿势评估，隐私安全且实时精准。
Privacy-Preserving Industrial Ergonomics: mmWave-Based Automated REBA Scoring and Pose Estimation
- 基于毫米波雷达的端到端多任务框架，重建3D人体姿态
- 高风险姿势识别准确率提升至MAE 0.93，推理延迟仅5.7毫秒
- 兼顾隐私保护与工业场景实用性，适合工厂智能监控
工作相关肌肉骨骼疾患（WMSDs）需持续进行人因工程评估。尽管快速全身评估（REBA）是金标准观察工具，但人工监测耗时费力，视觉方法则引发隐私担忧。本文提出一种基于毫米波雷达的隐私保护式人因工程评估新框架。该框架采用时空主干网络重建3D人体骨架，作为后续回归头生成REBA风险评分的生物力学基础。为应对雷达点云稀疏问题，引入结合生物力学约束与时间平滑性的多目标损失函数，并采用过采样策略缓解现有数据集中高风险姿势样本不平衡问题。在MMFi数据集上的实验表明，该框架实现77.78%分类准确率，推理延迟仅为5.70毫秒；高风险场景下REBA评分平均绝对误差达0.93，显著优于直接回归与两阶段流水线方案，提供了一种非侵入式工业人因评估的可靠解决方案。
原文摘要 · Abstract (English)
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.
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