Gesture recognition is a natural and device-free way of non-verbal communications. It has many use cases in different applications ranging from smart-homes to human-robot interactions. Different modalities can be used to realize a gesture-based interface from RGB cameras to LiDARs and Radars. Radars, specifically mmWave radars, are robust to different lighting and weather conditions, they have small form factor, and given the high frequency range, they can detect subtle movements. In this tutorial session, first, we will discuss the basic principles of mmWave Frequency-Modulated Continuous-Wave (FMCW) radars. Then, the data representation (Point Cloud) of the radar will be introduced and the challenges of processing this type of data will be discussed. Finally, using graph-based neural networks, we will train a model to recognize gestures by processing the point clouds generated by the radars. The in-person participants will have the opportunity to work with the radars that they will be provided to record real-world data and process them using the pipeline we will introduce during the tutorial.
Dariush Salami received his BSc and MSc degrees from Shahid Beheshti University and Amirkabir University of Technology in Software Engineering in 2016 and 2019, respectively. He is currently a Marie Skłodowska Curie fellow in ITN-WindMill project and a PhD researcher at the department of communications and networking at Aalto University. He is mainly focused on Machine Learning for Wireless Communications and Sensing especially in mmWave range using Frequency-Modulated Continuous-Wave (FMCW) radars for Human Centric Sensing (HCS).
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