Building a tinyML application touches on skill sets ranging from hardware engineering, embedded programming, software engineering, machine learning, data science and domain expertise about the application you are building. The steps required to build the application can be broken into four parts:
• Collecting and annotating data
• Applying signal preprocessing
• Training a classification algorithm
• Creating firmware optimized for the resource budget of an edge device
This talk will walk you through all the steps, and by the end of it we will have created an edge optimized TinyML application for the Arduino Nano 33 BLE Sense that is capable of recognizing different boxing punches in real-time using the Gyroscope and Accelerometer sensor data from the onboard IMU sensor.