Neethu Elizabeth Simon
Real-Time Gesture Recognition for Resource Constrained Devices using TinyML with TensorFlow Lite
Status: Coming up in April 2026!Advances in low power machine learning have enabled intelligent processing directly on microcontroller devices. This talk will present a fully on-device gesture recognition system deployed on an arm-based Arduino Nano 33 BLE board using tinyML with tensorflow lite for microcontrollers. The system demonstrates how accurate motion-classification models can operate within the severe memory and computational limits of an embedded platform without relying on cloud connectivity.
Data is collected using the multi-axis accelerometer sensor on the Arduino nano for various gestures which is then trained separately to build a tensorflow model based on a convolutional neural network (CNN). It is then converted to a tensorflow lite model and deployed on the Arduino nano for gesture recognition, achieving real-time inference at low latency and with minimal power consumption.
Audience will learn practical guidelines to build and deploy machine learning models for small, resource constrained embedded devices. This approach demonstrates the potential of tinyML for enabling intuitive human machine interfaces across wearables, consumer electronics, and industrial IoT sensing applications.