# Enabling Privacy-Aware AI-Based Ergonomic Analysis

arXiv: https://arxiv.org/abs/2505.07306 · HTML: https://mlsanity.com/paper/2505.07306/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 8 of 8)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [DeepPrivacy2: Towards Realistic Full-Body Anonymization](https://mlsanity.com/paper/2211.09454/) (2022)
- [Context-Aware Generative Adversarial Privacy](https://mlsanity.com/paper/1710.09549/) (2017)
- [Adversarial Scene Editing: Automatic Object Removal from Weak Supervision](https://mlsanity.com/paper/1806.01911/) (2018)
- [Privacy Protection in Street-View Panoramas using Depth and Multi-View Imagery](https://mlsanity.com/paper/1903.11532/) (2019)
- [Personal Privacy Protection via Irrelevant Faces Tracking and Pixelation in Video Live Streaming](https://mlsanity.com/paper/2101.01060/) (2021)
- [Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?](https://mlsanity.com/paper/2309.13038/) (2023)
- [Does Image Anonymization Impact Computer Vision Training?](https://mlsanity.com/paper/2306.05135/) (2023)

## Built on by (most cited first, 1 of 1)

- [Privacy-Preserving Computer Vision for Industry: Three Case Studies in Human-Centric Manufacturing](https://mlsanity.com/paper/2512.09463/) (2025)

## Abstract (arXiv)

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.