Continuous Gesture Recognition Based on Time Sequence Fusion Using MIMO Radar Sensor and Deep Learning
发布时间：2021-06-28
点击次数：
影响因子：2.412
DOI码：10.3390/electronics9050869
发表刊物：Electronics
关键字：gesture recognition; MIMO radar; deep learning; LSTM; CNN; feature fusion
摘要：Gesture recognition that is based on high-resolution radar has progressively developed in human-computer interaction field. In a radar recognition-based system, it is challenging to recognize various gesture types because of the lacking of gesture transversal feature. In this paper, we propose an integrated gesture recognition system that is based on frequency modulated continuous wave MIMO radar combined with deep learning network for gesture recognition. First, a pre-processing algorithm, which consists of the windowed fast Fourier transform and the intermediate-frequency signal band-pass-filter (IF-BPF), is applied to obtain improved Range Doppler Map. A range FFT based MUSIC (RFBM) two-dimensional (2D) joint super-resolution estimation algorithm is proposed to obtain a Range Azimuth Map to obtain gesture transversal feature. Range Doppler Map and Range Azimuth Map then respectively form a Range Doppler Map Time Sequence (RDMTS) and a Range Azimuth Map Time Sequence (RAMTS) in gesture recording duration. Finally, a Dual stream three-dimensional (3D) Convolution Neural Network combined with Long Short Term Memory (DS-3DCNN-LSTM) network is designed to extract and fuse features from both RDMTS and RAMTS, and then classify gestures with radial and transversal change. The experimental results show that the proposed system could distinguish 10 types of gestures containing transversal and radial motions with an average accuracy of 97.66%.
合写作者：蒋新月
第一作者：雷文太
论文类型：期刊论文
学科门类：工学
一级学科：信息与通信工程
文献类型：J
是否译文：否
发表时间：2020-05-23
收录刊物：SCI、EI