TY - GEN
T1 - Hand gesture recognition with ensemble time-frequency signatures using enhanced deep convolutional neural network
AU - Feng, Xiang
AU - Song, Qun
AU - Guo, Qingfang
AU - Liu, Duo
AU - Zhao, Zhanfeng
AU - Zhao, Yinan
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Hand gesture recognition using radar has been widely applied to control electronic appliances, military appliances and so on. In this paper, we investigate the feasibility of recognizing hand gestures using fused multiple time-frequency signatures, which ensembles micro-Doppler signatures, range-time signatures and angle-time signatures on spectrograms, with an Enhanced Deep Convolutional Neural Network (EDCNN). Several typical gestures included Tick, Double pushing, Rotating clockwise, and Rotating counterclockwise, were measured using Mm-wave radar and their spectrograms investigated. Therein EDCNN was employed to classify the spectrograms, with 80% of the data utilized for training and the remaining 20% for validation. Simulation said that the classification accuracy of the proposed method was found to be 96.2%.
AB - Hand gesture recognition using radar has been widely applied to control electronic appliances, military appliances and so on. In this paper, we investigate the feasibility of recognizing hand gestures using fused multiple time-frequency signatures, which ensembles micro-Doppler signatures, range-time signatures and angle-time signatures on spectrograms, with an Enhanced Deep Convolutional Neural Network (EDCNN). Several typical gestures included Tick, Double pushing, Rotating clockwise, and Rotating counterclockwise, were measured using Mm-wave radar and their spectrograms investigated. Therein EDCNN was employed to classify the spectrograms, with 80% of the data utilized for training and the remaining 20% for validation. Simulation said that the classification accuracy of the proposed method was found to be 96.2%.
UR - https://www.scopus.com/pages/publications/85082393620
U2 - 10.1109/APSIPAASC47483.2019.9023254
DO - 10.1109/APSIPAASC47483.2019.9023254
M3 - 会议稿件
AN - SCOPUS:85082393620
T3 - 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
SP - 1602
EP - 1605
BT - 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
Y2 - 18 November 2019 through 21 November 2019
ER -