Human activity classification using radar signal and RNN networks

Jiang, Haoyang; Fioranelli, F.; Yang, Shufan; Romain, Olivier; Le Kernec, Julien

Human activity classification using radar signal and RNN networks

Conference paper (2020)

Authors

Haoyang Jiang University of Electronic Science and Technology of China

F. Fioranelli

Shufan Yang University of Glasgow

Olivier Romain CY University

Julien Le Kernec University of Electronic Science and Technology of China, CY University, University of Glasgow

LSTM CLASSIFICATION HUMAN ACTIVITY RECOGNITION RADAR SIGNAL

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Published Date

2020

Language

English

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Abstract

Radar-based human activities recognition is still an open problem and is a key to detect anomalous behaviour for security and health applications. Deep learning networks such as convolutional neural networks (CNN) have been proposed for such tasks and showed better performance than traditional supervised learning paradigm. However, it is hard to deploy CNN networks to embedded systems due to the limited computational power available. From this point of concern, the use of a recurrent neural network (RNN) is proposed in this paper for human activities classification. We also propose an innovative data argumentation method to train the neural network using a limited number of data. The experiment shows that our network can achieve a mean accuracy of 94.3% in human activity classification.

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