Radar Based Activity Recognition using CNN-LSTM Network Architecture

A. Victoria, S. V. Manikanthan, R. VaradarajuH., Muhammad Alkirom Wildan, K. Kishore
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引用次数: 1

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.
基于CNN-LSTM网络结构的雷达活动识别
随着智能系统需求的不断发展,基于人类活动识别的研究得到了加强。已经有很多可穿戴设备和数字智能传感器被用来对各种活动进行分类。基于雷达传感器的活动识别是近年来研究的热点。为了对雷达微多普勒特征图像进行分类,提出了一种基于卷积神经网络-长短期记忆(CNN-LSTM)的分类方法。利用卷积层更新滤波值,学习雷达图像的特征。LSTM层除了增强卷积神经网络获得的特征外,还增强了时间信息。我们使用了格拉斯哥大学发布的数据集,该数据集捕获了56个不同年龄的受试者的六项活动,这是同类数据集中的第一个,不同于在受控实验室环境下捕获的信号。我们的模型对训练数据的准确率为96.8%,对测试数据的准确率为93.5%。所提出的工作优于现有的传统深度学习架构。
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