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

Published: 17 May 2024 Publication History

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.

References

[1]
Google Inc. 2017. Tensorflow Lite. Google Inc. https://github.com/tensorflow/tensorflow.
[2]
K. Bharath S. Rao S. S. Yadav, R. Agarwal and C. S. Thakur. 2022. tinyradar: mmwave radar based human activity classification for edge computing. IEEE International Symposium on Circuits and Systems (ISCAS) (2022), 2414–2417. https://doi.org/10.1109/ISCAS48785.2022.9937293
[3]
K. Bharath S. Rao S. S. Yadav, R. Agarwal and C. Singh Thakur. 2023. tinyradar for fitness: A contactless framework for edge computing. IEEE Transactions on Biomedical Circuits and Systems 17, 2 (2023), 192–201. https://doi.org/10.1109/TBCAS.2023.3244240
[4]
Texas Instruments 2023. IWRL6432 Single-Chip 57-to 64-GHz Industrial Radar Sensor. Texas Instruments. https://www.ti.com/lit/ds/symlink/iwrl6432.pdf.

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AIMLSystems '23: Proceedings of the Third International Conference on AI-ML Systems
October 2023
381 pages
ISBN:9798400716492
DOI:10.1145/3639856
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 May 2024

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  1. IWRL6432 single-chip mmWave radar
  2. depthwise separable convolution
  3. edge computing
  4. hand gesture recognition
  5. low power

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