AI-Powered Ergonomic Assessment with 3D Skeleton Tracking
Case Study Summary
Client: Confidential / Fortune 500 European Automotive Supplier
Website: —
Industry: Automotive (Manufacturing)
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.
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.
Our Approach
Built a depth-camera pipeline that converts 3D video into live skeletal keypoints and joint kinematics, then maps those to an ergonomics scoring rubric. 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.
Results & Impact
- Plant-wide assessment time reduced from 12 months to 3 weeks
- Live scoring during capture eliminated manual frame-by-frame measurements
- Faster identification of high-risk stations enabled earlier redesign and training
- Reusable pipeline enabled adjacent analytics, including cycle-time study work that contributed to a patent filing
Solution Overview
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 invoked from the Node.js app
- Rules Engine: Configurable rubric lookup for 7 risk classes (1: optimal, 7: not acceptable)
- Runtime: Local workstation capture with real-time scoring; batch export for reports (CSV/JSON)
- Data Handling: On-device processing; no personal data retention (GDPR compliance)
- Visualization: Overlayed skeleton and live score; anonymized exports (CSV/JSON) for BI tools
Additional Context
Timeline:
- Timeline: ~8 months (pilot to global rollout)
- Team Size: 4 people (Lead DS, DevOps Engineer, Data Scientist, Ergonomics Expert)
- Role: Lead Data Scientist
- Close collaboration with ergonomics team for rubric definition and validation; plant managers for scheduling and access.
- Future plans include automated report generation, trend dashboards, and optional LLM add-ons (natural-language summaries of sessions, SOP retrieval, and Q&A over telemetry).
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