Real-Time Activity Recognition using Embedded Machine Learning

Emily Glagolev, Mihir Kotas, and Ahmed Musani have developed a machine learning-based real-time activity recognition model for Arduino Nano. It was their final project for the Spring 2022 Embedded Machine Learning (BMI/CEN 598) course at Arizona State University (ASU). Embedded Machine Intelligence Lab (EMIL) is directed by Dr. Hassan Ghasemzadeh, who is an associate professor at ASU. Visit our website: https://ghasemzadeh.com