Live Demonstration: Real-time Gesture Recognition Using tinyRadar for Edge Computing

Yadav, SS and Singh Thakur, C and Adithya, MD and Anand, S and Munasala, M and Kankipati, D (2023) Live Demonstration: Real-time Gesture Recognition Using tinyRadar for Edge Computing. In: 3rd International Conference on AI-ML Systems, AIMLSystems 2023, 25 October 2023through 28 October 2023, Bangalore.

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Abstract

Hand gesture recognition (HGR) plays a pivotal role in improving human-machine interaction across domains like smart homes/vehicles and wearable devices. While vision-based HGR systems encounter challenges with lighting, complex backgrounds, and occlusion, radar-based systems overcome these limitations by harnessing electromagnetic principles. This demo paper presents tinyRadar, a real-time, low-power, single-chip radar solution for HGR. By leveraging miniaturized mmWave radar hardware, tinyRadar offers a compact and cost-effective HGR solution. The Texas Instruments IWRL6432 radar is utilized, achieving a total power consumption of less than 80mW and a memory footprint of �11 KB for the quantized inference model and < 256 KB for the entire system. The solution utilizes quantized depthwise separable convolutions and integrates a hardware accelerator and Cortex®-M4 microcontroller for real-time inference. With its small form factor and low power requirements, tinyRadar facilitates on-edge implementation, delivering 95 real-time inference accuracy for four gestures. This paper contributes to developing wearable gadgets and IoT devices that seamlessly incorporate HGR technology. © 2023 Owner/Author.

Item Type:	Conference Paper
Publication:	ACM International Conference Proceeding Series
Publisher:	Association for Computing Machinery
Additional Information:	The copyright for this article belongs to Association for Computing Machinery.
Keywords:	Automation; Computing power; Cost effectiveness; Edge computing; Gesture recognition; Intelligent buildings; Millimeter waves; Palmprint recognition, Depthwise separable convolution; Edge computing; Hand-gesture recognition; Human machine interaction; Iwrl6432 single-chip mmwave radar; Low Power; Mm waves; Real time gesture recognition; Real-time inference; Single-chip, Convolution
Department/Centre:	Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited:	22 Aug 2024 06:11
Last Modified:	22 Aug 2024 06:11
URI:	http://eprints.iisc.ac.in/id/eprint/85496

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