Radar Based Activity Recognition using CNN-LSTM Network Architecture

A. Helen Victoria; S.V. Manikanthan; Varadaraju H R; Muhammad Alkirom Wildan; Kakarla Hari Kishore

Author(s)

A. Helen Victoria Department of Networking and Communications, SRM Institute of Science and Technology, Chennai, India

S.V. Manikanthan Melange Academic Research Associates, Puducherry, India

Varadaraju H R Department of Electronics and Communication Engineering, Akshaya Institute of Technology, TUMKUR

Muhammad Alkirom Wildan Department of Management, Faculty of Economics and Business, University of Trunojoyo Madura, Bangkalan, 69162 Jawa Timur, Indonesia

Kakarla Hari Kishore Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India

Abstract

Human Activity Recognition based research has got intensified based on the evolving demand of smart systems. There has been already a lot of wearables, digital smart sensors deployed to classify various activities. Radar sensor-based Activity recognition has been an active research area during recent times. In order to classify the radar micro doppler signature images we have proposed a approach using Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Convolutional Layer is used to update the filter values to learn the features of the radar images. LSTM Layer enhances the temporal information besides the features obtained through Convolutional Neural Network. We have used a dataset published by University of Glasgow that captures six activities for 56 subjects under different ages, which is a first of its kind dataset unlike the signals captured under controlled lab environment. Our Model has achieved 96.8% for the training data and 93.5% for the testing data. The proposed work has outperformed the existing traditional deep learning Architectures.

Publication Details

Page(s) 303-312

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 14, Issue: 3, Year: 2022

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Keywords

deep learning, Micro doppler Signatures, Radar, Long Short-Term Memory