Buttonless Remote Control - Reproduce on your device"

Blair Newman
CTO
Neuton.AI

Danil Zherebtsov
Head of Machine Learning & Analytics
Neuton.AI

Tamas Daranyi
Platform Product Management
Silicon Labs

Edge Devices or Always-on devices often possess limited functionality, memory, and battery life that do not meet business requirements. This forces developers to find a balance between data analysis, functionality, complexity, energy capacity, and consumption. At Neuton.AI, we have developed an innovative approach to help people create compact neural networks that can recognize complex activities with minimal memory and energy consumption. In this tutorial, you will learn how to enhance the ability of users to control consumer electronics devices with just hand gestures and how to create a similar TinyML solution of only 4Kb in total footprint by yourself.
With a universal gesture-based remote control, you can easily access and control any Bluetooth-enabled media system or presentation slides without physical contact. Neuton's Gesture Recognition Model leveraging Silicon Labs xG24 Dev Kit for EFR32MG24 Wireless SoC can recognize eight different types of gestures with almost 99% accuracy, including swipe right, swipe left, double tap, double knock, clockwise rotation, counterclockwise rotation, idle, and an unknown class. Moreover, the Inference time for this model is less than 2.3 ms, and it has a memory footprint of only 4.2 KB in Flash and 1.4 KB RAM consumption.
This webinar will include:
- Deep dive into the solution creation process
- Different TinyML use cases' overview
- Silicon Labs product line presentation

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