What is Ergonomic Risk Analysis?
Ergonomic risk factors are workplace situations that cause wear and tear on the body and can cause injury. Common ergonomic hazards include repetition, awkward posture, high force, forceful motion, stationary position, direct pressure, vibration, and work stress.
Vision-based ergonomic risk analysis uses cameras to detect the human posture of workers. Deep learning methods detect the body parts and joints (semantic keypoint tracking) to analyze human movement in real-time or periodic snapshots. A camera-based ergonomic risk assessment can detect the occurrence of specific poses as well as the frequency of specific events.
Ergonomics Detection Features
Depending on the ergonomics use case, there are different deep learning methods to perform automated ergonomic risk assessments using video cameras. Modern, edge AI methods allow privacy-preserving image analysis with on-device processing: no video is sent to the cloud or stored locally, no human operators are needed.
- Deep learning models based on posture detection: Detect specifically trained postures such as sitting, standing, walking, laying down with frequency and duration per event.
- Keypoint-based analysis to track the body limbs and joints. The real-time tracking of key points involves heavy algorithms that require capable AI hardware.
- Track changes in human postures to identify specific activities and workflows.
- Measure, quantify and monitor specific ergonomic metrics and set up rule-based reporting.
Value of Ergonomics Recognition Systems
The recognition and monitoring of human postures using camera systems provide a highly efficient method for ergonomic risk assessments.
The detection and tracking of human positions as a wide range of use cases provide economic value through automation and digitization of human activity.
- Improve occupational safety and health while increasing productivity, efficiency and reducing the risk of business interruptions.
- Privacy-preserving real-time processing (Edge AI) without a human operator or sending videos to the cloud. No physical sensors are needed.
- AI-based vision systems automatically detect dangerous situations autonomously, using common security cameras.
- Save costs for manual inspection while increasing the data quality, accuracy, and objectivity of ergonomic risk assessments.
- Quantify the occurrence of specific events over a longer period of time, identify systematic issues across multiple workstations.
- A highly scalable method that can be easily implemented across multiple locations, plants, factories, and work floors.
