AI-Powered Gesture Recognition with Edge Impulse

This project implements real-time machine learning directly on a microcontroller using the IndusBoard Coin, combining hardware motion sensing with embedded artificial intelligence. At the core of the system is the IndusBoard Coin connected via a USB C-type cable for power and data transmission. Although the raw sensor integration relies on the board's internal capabilities, the overall workflow bridges embedded systems and cloud-based machine learning. First, motion data corresponding to specific physical movements or gestures is captured using the Edge Impulse CLI and associated software tools. This data acts as the dataset for training a custom neural network model within the Edge Impulse platform. Once trained to recognize distinct movement patterns, the resulting machine learning classifier is compiled into optimized C++ code and deployed directly onto the IndusBoard Coin using the Arduino IDE. During runtime, the board continuously samples physical motion data, feeds it into the onboard model, and performs real-time inference. The system processes these inputs locally at the edge—meaning no cloud connection is required for decision-making—allowing the IndusBoard Coin to instantly classify whether a given gesture matches a trained category such as a wave, tap, or rotation.