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Complex Gesture Detection at the Edge with Machine Learning
An innovative Human Machine Interface (HMI) is one of the best ways to differentiate your product from the competition. We now offer a solution that makes it easy to add complex gestures to your 2D touch interface. This means you can run a touchpad on a single Microchip microcontroller (MCU) while that same MCU classifies complex gestures in real time, delivering inference at the edge.
Whether you are new to Artificial Intelligence/Machine Learning (AI/ML) or an experienced user, we have a variety of easy-to-use solutions and a partner network to help you create your next cutting-edge design.
We have partnered with Motion Gestures to make advanced gesture recognition possible in a matter of minutes. If you can draw it with your fingertip, our solution will be able to recognize it. Motion Gestures provides powerful embedded AI-based gesture recognition software that is compatible with our 32-bit MCUs. It can be used with motion, touch and vision sensors and requires minimal computational resources to deliver highly accurate gesture recognition. Unlike conventional solutions, the gesture recognition engine uses advanced ML algorithms and does not require the collection of any training data. This powerful solution provides a gesture recognition accuracy of nearly 100% while significantly reducing your gesture software development time and costs.
Advanced Gesture Recognition Workflow
Motion Gestures offers a free library for complex gesture detection on our MCUs so you can easily test out gesture recognition in your application.
Demo of Motion Gestures-Based Complex Gesture Detection on a SAM C21 MCU
We make it easy to for you to experience the power and flexibility of ML-based complex gesture detection right out of the box. Our Motion Gestures-Based Complex Gesture Detection on a SAM C21 MCU demo is a complete development platform that includes the following components:
- Motion Gestures Demo
- Programmable HEX File, Demo GUI, User Guide
- Motion Gestures Demo Example Project
- MPLAB Project, User Guide
- SAM C21 Xplained Pro Evaluation Kit featuring an entry-level SAM C21 32-bit MCU
- QT8 Xplained Pro Extension Kit with touchpad
The demo uses the position tracking information from our 2D Touch Surface Library and includes a set of six complex gestures that can be recognized right out of the box:
- a checkmark
- a star
- the number 2
- the letters M, S and Alpha
The demo offers two options to explore gesture-based control:
- Complex gesture detection using the Motion Gestures platform
- A combination of simple gesture detection (swipes, pinch/zoom and tap events) from the 2D Touch Library and complex gesture detection
Machine-Learning-Based Gesture Recognition Demo Using dsPIC33C DSC
This demonstration application showcases machine-learning-based gesture recognition with a dsPIC33 Digital Signal Controller (DSC) interpreting and responding predictably to human gestures. The demo project outlines the process of data collection, transmission to the ML Model Builder, creation of a customized gesture recognition model for precise data classification, and deployment onto the DSC using MPLAB ML Development Suite.
Documentation
Machine Learning Video
This video describes Machine Learning-based solutions for touchpads and Inertial Measurement Units (IMUs).
