RadarNet: Efficient Gesture Recognition Technique Utilizing a Miniature Radar Sensor
Eiji Hayashi,Jaime Lien,Nicholas Gillian,Leonardo Giusti,Dave Weber,Jin Yamanaka,Lauren Bedal,Ivan Poupyrev +7 more
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TLDR
In this article, a convolutional neural network model was proposed to recognize four directional swipes and an omni-swipe using a miniaturized 60 GHz radar sensor.Abstract:
Gestures are a promising candidate as an input modality for ambient computing where conventional input modalities such as touchscreens are not available. Existing works have focused on gesture recognition using image sensors. However, their cost, high battery consumption, and privacy concerns made cameras challenging as an always-on solution. This paper introduces an efficient gesture recognition technique using a miniaturized 60 GHz radar sensor. The technique recognizes four directional swipes and an omni-swipe using a radar chip (6.5 × 5.0 mm) integrated into a mobile phone. We developed a convolutional neural network model efficient enough for battery powered and computationally constrained processors. Its model size and inference time is less than 1/5000 compared to an existing gesture recognition technique using radar. Our evaluations with large scale datasets consisting of 558,000 gesture samples and 3,920,000 negative samples demonstrated our algorithm’s efficiency, robustness, and readiness to be deployed outside of research laboratories.read more
Citations
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Journal ArticleDOI
Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
TL;DR: A big picture of the deep radar perception stack is provided, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion.
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Hand Gesture Recognition for an Off-the-Shelf Radar by Electromagnetic Modeling and Inversion
TL;DR: A novel data processing pipeline for hand gesture recognition that combines advanced full-wave electromagnetic modelling and inversion with machine learning is presented that enables the use of simple gesture recognition algorithms, such as template-matching recognizers, that can be trained in real time and provide competitive accuracy with only a few samples.
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Spiking Neural Network-Based Radar Gesture Recognition System Using Raw ADC Data
TL;DR: This work presents an embedded gesture recognition system using a 60 GHz frequency modulated continuous wave radar using spiking neural networks (SNNs) applied directly to raw analog-to-digital converter (ADC) data.
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Gesture Interaction in Virtual Reality
TL;DR: In this article, hand and body gestures are detected using human pose estimation based on off-the-shelf optical camera images using machine learning, and obtain reliable gesture recognition without additional sensors.
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Radar-Based Robust People Tracking and Consumer Applications
TL;DR: This work uses two popular algorithms, for tracking people using Radar, and compares them with a novel method, and shows that the method is robust in certain corner cases and is suitable for scenarios with many users in close proximity.
References
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