Smart pose estimation for radar based occupany detection
Accurately determine different human poses with our integrated radar edge AI optimized devices.
Application overview
Traditional sensing technologies like PIR, UWB, and ToF typically only provide basic presence information. Our radar sensing solutions leverage edge AI to enable advanced sensing capabilities like pose estimation. Allowing medical and home monitoring systems to notify help if someone has fallen or for applications like TVs and air conditioners to take actions based on if someone is standing, sitting, walking, or falling in real time.
Powered by our low power, cost optimized IWRL6432 radar, engineers can easily enable advanced pose estimation features to the next generation of home automation sensing.
Starting evaluation
Data collection
Capture accurate radar data with the IWRL6432 devices, enabling full AI processing done completely on the edge through the integrated M4F MCU and HWA.
Data quality assessment
Our radar sensors support a wide range of output formats, including range profiles, range-doppler heatmaps, point clouds, and more. For pose estimation, the model leverages object point cloud data to achieve high accuracy performance.
Customers can get started immediately using the sample dataset available in CCStudio™ Edge AI Studio which allows users to upload unfiltered point cloud data and experiment to optimize performance, as detailed in the pose estimation user guide.
Build and train your model
The radar extension in CCStudio™ Edge AI Studio is built around two key components:
- A front end GUI, available on a local system or cloud server
- A back-end PyTorch engine that handles API calls and model execution.
Beginners can start using our CCStudio™ Edge AI Studio to explore the radar surface classification example project. This guided experience includes a radar pre-loaded dataset evaluation, model definition, compilation, and other key steps in the workflow.
More advanced users who are comfortable with radar sensing and machine learning can interact directly with the back-end engine.
Training, compilation, and evaluation are fully customizable, allowing users to replace individual steps with their own methods as needed.
This flexible architecture ensures the tool delivers value to users at every level from those new to radar and machine learning to experienced developers seeking automation and integration guidance for deploying models into our radar firmware projects.
Deploying your model
Once trained and validated, CCStudio™ Edge AI Studio provides an end-to-end workflow for deploying trained models directly to our radar devices without any other systems needed.
All the hardware, software and resources you’ll need to get started
Hardware
IWRL6432
Single-chip low-power 57-GHz to 64-GHz industrial mmWave radar sensor
Software & development tools
CCStudio™ Edge AI Studio
Edge AI studio contains tools for training, compiling and deploying a model to TI edge AI-enabled devices.
Radar Toolbox for Edge AI
Learn more about how TI’s 60GHz and 77GHz industrial mmWave sensors can measure distance and relative velocities of people or objects.
Supporting resources
Learn more about pose estimation with our radar sensors using edge AI technology
Explore the latest advancements in sensing solutions. This demos showcases TI’s mmWave radar IWRL6432 enabled with edge AI to determine a human’s positioning. Learn how future building automation sensors can help build smarter and safer homes using AI enabled radar.