Embedded-Motion-Classification-System

Project overview

This project implements a real-time motion classification system on an STM32 microcontroller using a pretrained and quantized machine learning model generated with Edge Impulse (https://www.edgeimpulse.com/)

The system captures motion data from a sensor (accelerometer), performs preprocessing, and runs on-device inference to classify motion patterns.

Machine Learning Pipeline

1. Data collection using motion sensor
2. Dataset labeling
3. Model training in Edge Impulse
4. Model quantization (int8)
5. Deployment to STM32
6. Real-time inference on-device