Learn how to build a Human Activity Recognition (HAR) system on STM32 microcontrollers using accelerometer and gyroscope data. Train, optimize, and deploy real-time TinyML models on-device for ultra-low power motion classification.
STM32 TinyML Lab 3: Human Activity Recognition Using Accelerometer and Gyroscope Read More »
Learn TinyML on STM32! Deploy a neural network to predict sine waves using X-CUBE-AI. A beginner’s guide to embedded AI deployment on ARM Cortex-M. (154 characters)
Baby Steps into TinyML: Your First Embedded AI Project on STM32 Read More »
Learn PCA dimensionality reduction for STM32 TinyML. Optimize models using Python/scikit-learn & Iris data. Deploy with TFlite & X-Cube-AI.
TinyML Lab: Simple MLP + PCA for STM32 TinyML Deployment with X-Cube-AI Cloud Read More »
Train and deploy a simple 4-feature MLP for STM32 TinyML with IRIS dataset. Use Python/scikit-learn & Iris data. Deploy with ONNX/TFlite & X-Cube-AI.
TinyML Lab: Simple MLP for STM32 TinyML Deployment with X-Cube-AI Read More »
Master the STM32 X-CUBE-AI compiler. Deploy a Keras CNN model for Human Activity Recognition (HAR) onto the STM32U5, bypassing TFLM interpreter overhead for faster, leaner embedded AI.
TinyML Lab 2: Deploying Human Activity Recognition with X-CUBE-AI Read More »
