Human Gesture Classification for Autonomous Driving Applications using Radars

Ishak, Karim; Appenrodt, Nils; Dickmann, Jürgen; Waldschmidt, Christian

doi:10.1109/icmim48759.2020.9298980

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Radars have garnered a lot of attention as a sensor of choice due to their privacy-preserving features, ability to work within the enclosure, and their sensitivity to fine-grained gestures. There are two key aspects to radar-based gesture system solution: one being efficient miniature hardware that is capable of generating high-fidelity target data [15]- [22] and the other being the algorithm pipeline, propelled by deep learning, that parses target data to extract meaningful information of the user's intent [23]- [32].…”

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confidence: 99%

RadarSNN: A Resource Efficient Gesture Sensing System Based on mm-Wave Radar

Arsalan

Santra

Issakov

2022

IEEE Trans. Microwave Theory Techn.

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Radar offers a promising modality for enabling gesture recognition, which is a simple and intuitive alternative to click and touch-based human-computer interface. In this article, we propose a spiking neural network (SNN)-based hand gesture recognition with frequency-modulated continuous-wave 60-GHz radar. As preprocessing, the 2-D fast Fourier transform (FFT) is performed across fast time and slow time to generate a video of range-Doppler maps, which are then processed to generate range spectrograms, Doppler spectrograms, and angle spectrograms.The spike trains are fed into an SNN to classify the gesture that has been performed. We demonstrate that with few neurons, SNNs can achieve recognition accuracies close to 99.50% comparable to their deep learning counterparts for eight dynamic gestures. Moreover, the proposed model size is 75 kB, which is substantially smaller compared to the state-of-the-art models making it memory efficient. We also demonstrate using tSNE plots that SNNs can operate with lower embedding dimensions, implying that we can realize SNN with a small compute and memory footprint.

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mentioning

confidence: 99%

RadarSNN: A Resource Efficient Gesture Sensing System Based on mm-Wave Radar

Arsalan

Santra

Issakov

2022

IEEE Trans. Microwave Theory Techn.

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Radar-Based Gesture Recognition Towards Supporting Communication in Aphasia: The Bedroom Scenario

Santana

Rocha

Guimarães

et al. 2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Power-efficient gesture sensing for edge devices: mimicking fourier transforms with spiking neural networks

2022

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One of the key design requirements for any portable/mobile device is low power. To enable such a low powered device, we propose an embedded gesture detection system that uses spiking neural networks (SNNs) applied directly to raw ADC data of a 60GHz frequency modulated continuous wave radar. SNNs can facilitate low power systems because they are sparse in time and space and are event-driven. The proposed system, as opposed to earlier state-of-the-art methods, relies solely on the target’s raw ADC data, thus avoiding the overhead of performing slow-time and fast-time Fourier transforms (FFTs) processing. The proposed architecture mimics the discrete Fourier transformation within the SNN itself avoiding the need for FFT accelerators and makes the FFT processing tailored to the specific application, in this case gesture sensing. The experimental results demonstrate that the proposed system is capable of classifying 8 different gestures with an accuracy of 98.7%. This result is comparable to the conventional approaches, yet it offers lower complexity, lower power consumption and faster computations comparable to the conventional approaches.

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Human Gesture Classification for Autonomous Driving Applications using Radars

Cited by 3 publications

References 9 publications

RadarSNN: A Resource Efficient Gesture Sensing System Based on mm-Wave Radar

RadarSNN: A Resource Efficient Gesture Sensing System Based on mm-Wave Radar

Radar-Based Gesture Recognition Towards Supporting Communication in Aphasia: The Bedroom Scenario

Power-efficient gesture sensing for edge devices: mimicking fourier transforms with spiking neural networks

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