Continuous Gesture Recognition Based on Time Sequence Fusion Using MIMO Radar Sensor and Deep Learning

Release time:2021-06-28

Impact Factor:2.412

DOI number:10.3390/electronics9050869

Journal:Electronics

Key Words:gesture recognition; MIMO radar; deep learning; LSTM; CNN; feature fusion

Abstract: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%.

Co-author:蒋新月

First Author:雷文太

Indexed by:Journal paper

Discipline:Engineering

First-Level Discipline:Information and Communication Engineering

Document Type:J

Translation or Not:no

Date of Publication:2020-05-23

Included Journals:SCI、EI