Track and analyze users' body movements to provide feedback on exercise with STM32N6 at 28 FPS.
RGB Image sensor.
In the STM32 Model Zoo, we provide the code example with two different image sensors: Sony IMX335 5Mp RGB (part of the STM32N6570-DK) or ST's VD66GY 1.5 Mp RGB Global shutter (ideal for precise movement capture).
Dataset Internal (ST-modified Coco 201 Person)
Model Yolov8n_pose (implemented in Pytorch by Ultralytics and quantized in int8 format using tensorflow lite converter). Input size: 256 x 256 x 3
Weights: 3.2 MB
Activations: 1.1 MB
Inference time: 36 ms
Inference per second: 28
Pose mAP50 (mean Average Precision): 51.1 %
The yolov8 is a state of the art model for multi-pose estimation. Different tradeoff can be achieved by using other dataset, model or resolution. Balancing these factors is crucial for optimizing AI models to meet specific performance and hardware resource constraints. Find more models in the STM32 Model zoo.
Easily recreate this use-case with the following resources:
- Step by step tutorial
- Yolov8n_pose pre-trained model
- Application code example
Additional resources:
- How to install the STM32 Model zoo
Author: Vincent RICHARD / Last updated: January, 2025
A free STM32Cube expansion package, X-CUBE-AI allows developers to convert pretrained AI algorithms automatically, such as neural network and machine learning models, into optimized C code for STM32.
The STM32 family of 32-bit microcontrollers based on the Arm Cortex®-M processor is designed to offer new degrees of freedom to MCU users. It offers products combining very high performance, real-time capabilities, digital signal processing, low-power / low-voltage operation, and connectivity, while maintaining full integration and ease of development.