Activity Classification Using Raw Range and I & Q Radar Data with Long Short Term Memory Layers

Loukas, Charalampos; Fioranelli, Francesco; Le Kernec, Julien; Yang, Shufan

Abstract

This paper presents the first initial results of using radar raw I & Q data and range profiles combined with Long Short Term Memory layers to classify human activities. Although tested only on simple classification problems, this is an innovative approach that enables to bypass the conventional usage of Doppler-time patterns (spectrograms) as inputs of the Long Short Term Memory layers, and adopt instead sequences of range profiles or even raw complex data as inputs. A maximum 99.56% accuracy and a mean accuracy of 97.67% was achieved by treating the radar data as these time sequences, in an effective scheme using a deep learning approach that did not require the pre-processing of the radar data to generate spectrograms and treat them as images. The prediction time needed for a given input testing sample is also reported, showing a promising path for real-time implementation once the Long Short Term Memory layers network is properly trained.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)
• Start Date: Aug 12, 2018
• End Date: Aug 15, 2018
• Online Publication Date: Oct 29, 2018
• Publication Date: Oct 29, 2018
• Deposit Date: Mar 12, 2021
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)
• ISBN: 9781538675182
• DOI: https://doi.org/10.1109/dasc/picom/datacom/cyberscitec.2018.00088
• Keywords: radar, deep learning, Human Activity Recognition (HAR), LSTM
• Public URL: http://researchrepository.napier.ac.uk/Output/2752451