In order to provide an alternative solution of human machine interfaces, this paper proposed to recognize 10 human hand gestures regularly used in the consumer electronics controlling scenarios based on a three-dimensional radar array. This radar array was composed of three low cost 24GHz K-band Doppler CW (Continuous Wave) miniature I/Q (In-phase and Quadrature) transceiver sensors perpendicularly mounted to each other. Temporal and spectral analysis was performed to extract magnitude and phase features from six channels of I/Q signals. Two classifiers were proposed to implement the recognition. Firstly, a decision tree classifier performed a fast responsive recognition by using the supervised thresholds. To improve the recognition robustness, this paper further studied the recognition using a two layer CNN (Convolutional Neural Network) classifier with the frequency spectra as the inputs. Finally, the paper demonstrated the experiments and analysed the performances of the radar array respectively. Results showed that the proposed system could reach a high recognition accurate rate higher than 92%.
Shengchang LAN
Harbin Institute of Technology
Zonglong HE
Korea Advanced Institute of Science and Technology (KAIST)
Weichu CHEN
Harbin Institute of Technology
Kai YAO
Harbin Institute of Technology
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Shengchang LAN, Zonglong HE, Weichu CHEN, Kai YAO, "A Low Cost Solution of Hand Gesture Recognition Using a Three-Dimensional Radar Array" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 2, pp. 233-240, February 2019, doi: 10.1587/transcom.2018ISP0007.
Abstract: In order to provide an alternative solution of human machine interfaces, this paper proposed to recognize 10 human hand gestures regularly used in the consumer electronics controlling scenarios based on a three-dimensional radar array. This radar array was composed of three low cost 24GHz K-band Doppler CW (Continuous Wave) miniature I/Q (In-phase and Quadrature) transceiver sensors perpendicularly mounted to each other. Temporal and spectral analysis was performed to extract magnitude and phase features from six channels of I/Q signals. Two classifiers were proposed to implement the recognition. Firstly, a decision tree classifier performed a fast responsive recognition by using the supervised thresholds. To improve the recognition robustness, this paper further studied the recognition using a two layer CNN (Convolutional Neural Network) classifier with the frequency spectra as the inputs. Finally, the paper demonstrated the experiments and analysed the performances of the radar array respectively. Results showed that the proposed system could reach a high recognition accurate rate higher than 92%.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018ISP0007/_p
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@ARTICLE{e102-b_2_233,
author={Shengchang LAN, Zonglong HE, Weichu CHEN, Kai YAO, },
journal={IEICE TRANSACTIONS on Communications},
title={A Low Cost Solution of Hand Gesture Recognition Using a Three-Dimensional Radar Array},
year={2019},
volume={E102-B},
number={2},
pages={233-240},
abstract={In order to provide an alternative solution of human machine interfaces, this paper proposed to recognize 10 human hand gestures regularly used in the consumer electronics controlling scenarios based on a three-dimensional radar array. This radar array was composed of three low cost 24GHz K-band Doppler CW (Continuous Wave) miniature I/Q (In-phase and Quadrature) transceiver sensors perpendicularly mounted to each other. Temporal and spectral analysis was performed to extract magnitude and phase features from six channels of I/Q signals. Two classifiers were proposed to implement the recognition. Firstly, a decision tree classifier performed a fast responsive recognition by using the supervised thresholds. To improve the recognition robustness, this paper further studied the recognition using a two layer CNN (Convolutional Neural Network) classifier with the frequency spectra as the inputs. Finally, the paper demonstrated the experiments and analysed the performances of the radar array respectively. Results showed that the proposed system could reach a high recognition accurate rate higher than 92%.},
keywords={},
doi={10.1587/transcom.2018ISP0007},
ISSN={1745-1345},
month={February},}
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TY - JOUR
TI - A Low Cost Solution of Hand Gesture Recognition Using a Three-Dimensional Radar Array
T2 - IEICE TRANSACTIONS on Communications
SP - 233
EP - 240
AU - Shengchang LAN
AU - Zonglong HE
AU - Weichu CHEN
AU - Kai YAO
PY - 2019
DO - 10.1587/transcom.2018ISP0007
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 2
JA - IEICE TRANSACTIONS on Communications
Y1 - February 2019
AB - In order to provide an alternative solution of human machine interfaces, this paper proposed to recognize 10 human hand gestures regularly used in the consumer electronics controlling scenarios based on a three-dimensional radar array. This radar array was composed of three low cost 24GHz K-band Doppler CW (Continuous Wave) miniature I/Q (In-phase and Quadrature) transceiver sensors perpendicularly mounted to each other. Temporal and spectral analysis was performed to extract magnitude and phase features from six channels of I/Q signals. Two classifiers were proposed to implement the recognition. Firstly, a decision tree classifier performed a fast responsive recognition by using the supervised thresholds. To improve the recognition robustness, this paper further studied the recognition using a two layer CNN (Convolutional Neural Network) classifier with the frequency spectra as the inputs. Finally, the paper demonstrated the experiments and analysed the performances of the radar array respectively. Results showed that the proposed system could reach a high recognition accurate rate higher than 92%.
ER -