Gesture Classification with Handcrafted Micro-Doppler Features using a FMCW Radar

Sun, Yuliang; Fei, Tai; Schliep, F.; Pohl, Nils

doi:10.1109/icmim.2018.8443507

Cited by 49 publications

(30 citation statements)

References 6 publications

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“…The main difference between FFT and STFT is that STFT separates FFT operations in chunks across time. STFT has been used to retrieve phase information across time [60], [65], for creation of time and frequency spectrograms [64], [113], [116], and to create range and micro-velocity or micro-Doppler spectrograms from a stack of range profiles [75], [78], [92], [93], [129] or directly from a signal containing Doppler information [102], [119]. Microvelocity spectrograms are used to analyze fine-grained velocity features.…”

Section: Time Domain Frequency Domainmentioning

confidence: 99%

“…Pulse integration uses the integration operation with the purpose of improving signal-to-noise ratio [128], [179], help discover movement in a spectrogram [106], and to deduce a spectrogram representing micro-doppler signatures [129], [130]. Pulse integration types include incoherent integration [106], coherent integration [128], spectrogram integration across range [129], [130], and a wideband signal filter operation [179]. POSP is used in [141] to calculate an integral.…”

Section: Time Domain Frequency Domainmentioning

confidence: 99%

“…Transfer learning [120], [181], [193] is not included in this section since the methodologies transfer analytical model parameters to another analytical model rather than extracted data features. Feature analysis [43], [77], [91], [129] was only addressed by a minority of papers and is therefore excluded from this paper as well. Table X presents our analysis of feature extraction approaches used in millimeter wave sensing applications.…”

Section: Feature Extractionmentioning

confidence: 99%

See 2 more Smart Citations

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

et al. 2021

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The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300 GHz that correspond to wavelengths ranging from 10 to 1 mm. Although millimeter wave is often considered as a communication medium, it has also proved to be an excellent 'sensor', thanks to its narrow beams, operation across a wide bandwidth, and interaction with atmospheric constituents. In this paper, which is to the best of our knowledge the first review that completely covers millimeter wave sensing application pipelines, we provide a comprehensive overview and analysis of different basic application pipeline building blocks, including hardware, algorithms, analytical models, and model evaluation techniques. The review also provides a taxonomy that highlights different millimeter wave sensing application domains. By performing a thorough analysis, complying with the systematic literature review methodology and reviewing 165 papers, we not only extend previous investigations focused only on communication aspects of the millimeter wave technology and using millimeter wave technology for active imaging, but also highlight scientific and technological challenges and trends, and provide a future perspective for applications of millimeter wave as a sensing technology.

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Section: Time Domain Frequency Domainmentioning

confidence: 99%

Section: Time Domain Frequency Domainmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

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Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

et al. 2021

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“…To obtain the target micro‐Doppler signature, we first sum the data over the range bins of interest as

V false(m false) = \sum_{r = r_{1}}^{r_{2}} R false(r, m false)

where

r_{1}

and

r_{2}

are the minimum and maximum range bins considered, set to 10 and 128, respectively. This corresponds to a range swath from 0.75 to 9.6 m [32]. The short‐time Fourier transform is then applied to

V false(m false)

, and its magnitude square, i.e.…”

Section: Radar System and Data Analysismentioning

confidence: 99%

Radar classifications of consecutive and contiguous human gross‐motor activities

Amin

Guendel

2020

IET Radar, Sonar & Navigation

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“…Electromagnetic sensor-based: Electromagnetic sensors have been widely used for human activity classification [7,8,16,17] and gesture recognition [18][19][20][21][22][23][24][25][26]. Most of the proposed methods for gesture recognition based on electromagnetic sensors can only recognized hand gestures of big movements, but some methods for classifying finger gestures have also been reported, such as WiFinger's method [23] and project Soli [22,27].…”

Section: Introductionmentioning

confidence: 99%

A Method of Ultrasonic Finger Gesture Recognition Based on the Micro-Doppler Effect

Zeng

Kuang

et al. 2019

Applied Sciences

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With the popularity of small-screen smart mobile devices, gestures as a new type of human–computer interaction are highly demanded. Furthermore, finger gestures are more familiar to people in controlling devices. In this paper, a new method for recognizing finger gestures is proposed. Ultrasound was actively emitted to measure the micro-Doppler effect caused by finger motions and was obtained at high resolution. By micro-Doppler processing, micro-Doppler feature maps of finger gestures were generated. Since the feature map has a similar structure to the single channel color image, a recognition model based on a convolutional neural network was constructed for classification. The optimized recognition model achieved an average accuracy of 96.51% in the experiment.

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Gesture Classification with Handcrafted Micro-Doppler Features using a FMCW Radar

Cited by 49 publications

References 6 publications

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

Radar classifications of consecutive and contiguous human gross‐motor activities

A Method of Ultrasonic Finger Gesture Recognition Based on the Micro-Doppler Effect

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