Enabling Privacy-Aware AI-Based Ergonomic Analysis

TL;DR

This paper presents a privacy-preserving ergonomic assessment system using adversarially trained neural networks to obfuscate video data, enabling accurate pose estimation and risk evaluation without compromising worker privacy.

Contribution

It introduces a novel adversarial training approach to develop lightweight neural networks that obfuscate video data for privacy while maintaining pose estimation accuracy.

Findings

High accuracy in pose estimation with obfuscated videos

Effective 3D reconstruction using multi-view data

Enhanced privacy protection in industrial ergonomic monitoring

Abstract

Musculoskeletal disorders (MSDs) are a leading cause of injury and productivity loss in the manufacturing industry, incurring substantial economic costs. Ergonomic assessments can mitigate these risks by identifying workplace adjustments that improve posture and reduce strain. Camera-based systems offer a non-intrusive, cost-effective method for continuous ergonomic tracking, but they also raise significant privacy concerns. To address this, we propose a privacy-aware ergonomic assessment framework utilizing machine learning techniques. Our approach employs adversarial training to develop a lightweight neural network that obfuscates video data, preserving only the essential information needed for human pose estimation. This obfuscation ensures compatibility with standard pose estimation algorithms, maintaining high accuracy while protecting privacy. The obfuscated video data is transmitted to a central server, where state-of-the-art keypoint detection algorithms extract body landmarks. Using multi-view integration, 3D keypoints are reconstructed and evaluated with the Rapid Entire Body Assessment (REBA) method. Our system provides a secure, effective solution for ergonomic monitoring in industrial environments, addressing both privacy and workplace safety concerns.