Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes

Amin, Moeness G.; Zeng, Zhengxin; Shan, Tao

doi:10.48550/arxiv.1811.12467

Cited by 1 publication

(2 citation statements)

References 23 publications

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“…Some studies [6], [7] tackle the above problem by using continuous wave (CW) Radars, which can measure the radialvelocity between sensor and moving arm, but not the range.…”

Section: Introductionmentioning

confidence: 99%

“…A moving arm is such a target and in many studies a mD spectrogram, which contains the signature of the movement, is utilized. In [5], [7], [9], [10] empirical features are developed to extract as much information from the spectrogram as possible, including the length of a gesture, the ratio of negative to positive Doppler frequency, the bandwidth of mD frequency and the spectral power distribution. In [11], the authors make use of the fact that such a spectrogram is sparse and apply the Orthogonal Matching Pursuit (OMP) algorithm to extract features.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Macro Gesture Recognition Using Efficient Empirical Feature Extraction With Millimeter-Wave Technology

2021

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Human Machine Interaction based on air gestures finds an increasing number of applications in consumer electronics. The availability of mmWave technology, combined with machine learning, allows the detection and classification of gestures, avoiding high-resolution LIDAR or video sensors. Nevertheless, in most of the existing studies, the processing takes place offline, takes into account only the velocity and distance of the moving arm, and can handle only gestures that are conducted very close to the sensor device, which limits the range of possible applications. Here, we use an experimental multi-channel mmWave-based system that can detect small targets, like a moving arm, up to a few meters away from the sensor. As our pipeline can estimate and take into account the angle of arrival in azimuth and elevation, it has the ability to classify a greater variety of dynamic gestures. Furthermore, the digital signal processing chain we present here, runs in real-time, incorporating an event detector. Whenever an event is detected, a novel empirical feature extraction takes place and a Multi-Layer Perceptron is deployed to infer the type of the gesture. To evaluate our setup and signal processing pipeline, a dataset with ten subjects, performing nine gestures was recorded. Our method yielded 94.3% accuracy on the test set, indicating a successful combination of our proposed sensor and signal processing pipeline for real time applications.

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“…Some studies [6], [7] tackle the above problem by using continuous wave (CW) Radars, which can measure the radialvelocity between sensor and moving arm, but not the range.…”

Section: Introductionmentioning

confidence: 99%