AI-Powered Ergonomic Assessment with 3D Skeleton Tracking
Case Study Summary
Client: Confidential / Fortune 500 European Automotive Supplier
Industry: Automotive (Manufacturing)
Role: Lead Data Scientist
Impact Metrics:
- −94% study duration per plant (12 months → 3 weeks)
- ≈17× throughput increase (workstations assessed per week)
- Real-time in-session ergonomic scoring
- Zero post-hoc manual angle measurement
- Standardized scoring rubric across sites
The ergonomics team needed plant-wide ergonomics assessments in weeks, not months; we delivered an AI-powered 3D vision workflow that lets ergonomists identify high risk workstations quickly and consistently at plant scale.
The Challenge
Manual video review and angle measurement made ergonomic studies slow and error-prone. Plants had hundreds of workstations and repetitive tasks with musculoskeletal risk. One plant needed roughly a year to complete a full study, delaying mitigation and inflating risk exposure.
The Solution
→ Implementation
I built a depth-camera pipeline that converts 3D video into live skeletal keypoints and joint kinematics, then maps those to an ergonomics scoring rubric. The pipeline executes in stages: capture → 3D pose estimation → kinematic features (angles/rotations) → rules engine (rubric lookup) → real-time score & report. The system delivered in-session feedback and standardized outputs the ergonomics team could act on immediately.
→ Solution Architecture
Depth camera → skeletal tracking SDK → kinematics engine → rules/scoring service → dashboard/report export.
→ Tech Stack
- Cloud: Microsoft Azure Cloud Infrastructure
- CI/CD: Azure DevOps Pipelines
- Containerization: Docker
- Pose Detection: Azure Kinect Body Tracking SDK (C#, 3D joints), Intel RealSense depth
- Application/UI: Node.js app handling capture control, live overlays, and operator workflow
- Backend: C# SDK bridge for camera integration; Python modules for kinematics and scoring
- Kinematics & Scoring: Python modules for joint angles, ranges of motion, and rotations
- Rules Engine: Configurable rubric lookup for 7 risk classes (1: optimal, 7: not acceptable)
- Data Handling: On-device processing; no personal data retention (GDPR compliance)
Key Learnings
- Real-time Edge Processing: Optimized 3D pose estimation and kinematic calculations to run locally on workstations, enabling immediate feedback.
- GDPR Compliance: Designed the system to process video data in-memory without retention, storing only anonymous skeletal data and scores to satisfy strict privacy rules.
- Cross-Disciplinary Validation: Collaborated deeply with ergonomics experts to translate subjective scoring sheets into deterministic geometric rules.
- Standardization: Replaced subjective human observation with consistent, objective measurements across multiple global plant sites.
Measurable Impact
- −94% Study Time: Plant-wide assessment time reduced from 12 months to 3 weeks.
- Real-time Efficiency: Live scoring during capture eliminated manual frame-by-frame measurements.
- Proactive Safety: Faster identification of high-risk stations enabled earlier redesign and training.
- Innovation: Reusable pipeline enabled adjacent analytics, including cycle-time study work that contributed to a patent filing.
Let's have a virtual coffee together!
Want to see if we're a match? Let's have a chat and find out. Schedule a free 30-minute strategy session to discuss your AI challenges and explore how we can work together.