3 research outputs found
Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform
In this paper, a real-time signal processing frame-work based on a 60 GHz
frequency-modulated continuous wave (FMCW) radar system to recognize gestures
is proposed. In order to improve the robustness of the radar-based gesture
recognition system, the proposed framework extracts a comprehensive hand
profile, including range, Doppler, azimuth and elevation, over multiple
measurement-cycles and encodes them into a feature cube. Rather than feeding
the range-Doppler spectrum sequence into a deep convolutional neural network
(CNN) connected with recurrent neural networks, the proposed framework takes
the aforementioned feature cube as input of a shallow CNN for gesture
recognition to reduce the computational complexity. In addition, we develop a
hand activity detection (HAD) algorithm to automatize the detection of gestures
in real-time case. The proposed HAD can capture the time-stamp at which a
gesture finishes and feeds the hand profile of all the relevant
measurement-cycles before this time-stamp into the CNN with low latency. Since
the proposed framework is able to detect and classify gestures at limited
computational cost, it could be deployed in an edge-computing platform for
real-time applications, whose performance is notedly inferior to a
state-of-the-art personal computer. The experimental results show that the
proposed framework has the capability of classifying 12 gestures in real-time
with a high F1-score.Comment: Accepted for publication in IEEE Sensors Journal. A video is
available on https://youtu.be/IR5NnZvZBL
Automatic Radar-based Gesture Detection and Classification via a Region-based Deep Convolutional Neural Network
In this paper, a region-based deep convolutional neural network (R-DCNN) is proposed to detect and classify gestures measured by a frequency-modulated continuous wave radar system. Micro-Doppler (μD) signatures of gestures are exploited, and the resulting spectrograms are fed into a neural network. We are the first to use the R-DCNN for radar-based gesture recognition, such that multiple gestures could be automatically detected and classified without manually clipping the data streams according to each hand movement in advance. Further, along with the μD signatures, we incorporate phase-difference information of received signals from an L-shaped antenna array to enhance the classification accuracy. Finally, the classification results show that the proposed network trained with spectrogram and phase-difference information can guarantee a promising performance for nine gestures