What is Posture Recognition?
Computer vision algorithms perform image processing on real-time video feeds to detect specific human postures. Human pose estimation makes use of semantic keypoint tracking in video frames.
Posture Recognition Features
Detection of human activity and movement by applying posture recognition to video feeds.
- AI models to detect the following posture states: Laying down, sitting, walking, and standing.
- Track changes in human postures to determine specific events (standing up, seating down, falling down)
- Determine time between specific posture-changing events.
Value of Posture Recognition Systems
The detection and tracking of human positions as a wide range of use cases that provide economic value through automation and digitization of human activity.
- The use of conventional cameras is non-invasive, no physical sensors are needed.
- On-device processing of visuals ensures privacy-preserving image processing (no data-offloading to the cloud).
- Automatically detect specific events and suspicious behavior in security and surveillance applications.
- Workforce safety application to detect dangerous situations autonomously.
- Save costs for manual observation while obtaining higher accuracy and repeatability.
- Quantification of the occurrence of specific events based on posture recognition.
- Healthcare applications to determine the time spent between key events seating, standing, walking (e.g. Timed Stand Up An Go Test)