Automatic Arm Motion Recognition Using Radar for Smart Home Technologies

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

doi:10.1109/radar41533.2019.171318

Cited by 4 publications

(10 citation statements)

References 32 publications

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“…In essence, by applying the DTW method, the two time series which belong to the same motion class assume small distance and high similarity, which reduces the probability of misclassification. In our previous work [16,17], each of the original extracted envelope features contained 2000 samples for both positive and negative Doppler frequencies, and were directly input into the NN classifier with the L1 distance measure, achieving an overall accuracy 97.17% [16]. Considering the real-time processing and to avoid high computational burden of DTW dealing with long time series, we downsampled the envelopes to 200 samples.…”

Section: Classification Accuracy Of the Dtw Methodsmentioning

confidence: 99%

“…In this respect, the envelopes can accurately characterize different arm motions. An energy-based thresholding algorithm discussed in [17,44] can be applied to extract the envelopes. First, the maximum positive and negative Doppler frequencies are determined by computing the effective bandwidth of each motion from the spectrogram.…”

Section: Extraction Of the Maximum Instantaneous Doppler Frequency Simentioning

confidence: 99%

“…In this paper, with a focus on improving the results in [17], we employ features that capture the MD signature envelope behavior as well as the evolution characteristics. The envelope represents the maximum instantaneous Doppler frequencies, and thus, can be considered as a time series.…”

Section: Introductionmentioning

confidence: 99%

“…Both time-series analysis methods are robust to time misalignment. Similar to [17], our feature vector includes the augmented positive and negative frequency envelopes. However, we also augment these two envelopes with a vector of their differences which properly captures the time synchronization nature of the two envelopes.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes

2020

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Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man-machine interfaces and smart environments. In this paper, we use the time-series analysis method to accurately measure the similarity of the micro-Doppler (MD) signatures between the training and test data, thus providing improved gesture classification. We characterize the MD signatures by the maximum instantaneous Doppler frequencies depicted in the spectrograms. In particular, we apply two machine learning (ML) techniques, namely, the dynamic time warping (DTW) method and the long short-term memory (LSTM) network. Both methods take into account the values as well as the temporal evolution and characteristics of the time-series data. It is shown that the DTW method achieves high gesture classification rates and is robust to time misalignment.

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Section: Classification Accuracy Of the Dtw Methodsmentioning

confidence: 99%

Section: Extraction Of the Maximum Instantaneous Doppler Frequency Simentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes

2020

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“…In this paper, and towards improving on the results in [17] , we employ features which capture the MD signature envelope behavior and its evolution characteristics. In partic-ular, considering the envelope as a time-series or a curve, we measure the similarity between curves in a way that takes into account both the location and ordering of the points along the curve.…”

Section: Introductionmentioning

confidence: 99%

Arm Motion Classification Using Curve Matching of Maximum Instantaneous Doppler Frequency Signatures

Amin¹,

Zeng²,

Shan³

2019

Preprint

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Hand and arm gesture recognition using the radio frequency (RF) sensing modality proves valuable in manmachine interface and smart environment. In this paper, we use curve matching techniques for measuring the similarity of the maximum instantaneous Doppler frequencies corresponding to different arm gestures. In particular, we apply both Fréchet and dynamic time warping (DTW) distances that, unlike the Euclidean (L2) and Manhattan (L1) distances, take into account both the location and the order of the points for rendering two curves similar or dissimilar. It is shown that improved arm gesture classification can be achieved by using the DTW method, in lieu of L2 and L1 distances, under the nearest neighbor (NN) classifier.

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Arm Motion Classification Using Curve Matching of Maximum Instantaneous Doppler Frequency Signatures

Amin

Zeng

Shan

2020

2020 IEEE International Radar Conference (RADAR)

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Automatic Arm Motion Recognition Using Radar for Smart Home Technologies

Cited by 4 publications

References 32 publications

Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes

Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes

Arm Motion Classification Using Curve Matching of Maximum Instantaneous Doppler Frequency Signatures

Arm Motion Classification Using Curve Matching of Maximum Instantaneous Doppler Frequency Signatures

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