Deep Learning Techniques for Radar-Based Continuous Human Activity Recognition

Ruchita Mehta, Sara Sharifzadeh, Vasile Palade, Bo Tan, Alireza Daneshkhah, Yordanka Karayaneva

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Abstract

Human capability to perform routine tasks declines with age and age-related problems. Remote human activity recognition (HAR) is beneficial for regular monitoring of the elderly population. This paper addresses the problem of the continuous detection of daily human activities using a mm-wave Doppler radar. In this study, two strategies have been employed: the first method uses un-equalized series of activities, whereas the second method utilizes a gradient-based strategy for equalization of the series of activities. The dynamic time warping (DTW) algorithm and Long Short-term Memory (LSTM) techniques have been implemented for the classification of un-equalized and equalized series of activities, respectively. The input for DTW was provided using three strategies. The first approach uses the pixel-level data of frames (UnSup-PLevel). In the other two strategies, a convolutional variational autoencoder (CVAE) is used to extract Un-Supervised Encoded features (UnSup-EnLevel) and Supervised Encoded features (Sup-EnLevel) from the series of Doppler frames. The second approach for equalized data series involves the application of four distinct feature extraction methods: i.e., convolutional neural networks (CNN), supervised and unsupervised CVAE, and principal component Analysis (PCA). The extracted features were considered as an input to the LSTM. This paper presents a comparative analysis of a novel supervised feature extraction pipeline, employing Sup-ENLevel-DTW and Sup-EnLevel-LSTM, against several state-of-the-art unsupervised methods, including UnSUp-EnLevel-DTW, UnSup-EnLevel-LSTM, CNN-LSTM, and PCA-LSTM. The results demonstrate the superiority of the Sup-EnLevel-LSTM strategy. However, the UnSup-PLevel strategy worked surprisingly well without using annotations and frame equalization.
Original languageEnglish
Pages (from-to)1493-1518
Number of pages26
JournalMachine Learning and Knowledge Extraction
Volume5
Issue number4
Early online date14 Oct 2023
DOIs
Publication statusE-pub ahead of print - 14 Oct 2023

Bibliographical note

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Funder

This research was funded by Coventry University, UK.

Keywords

  • convolutional variational autoencoder (CVAE)
  • deep neural networks (DNNs)
  • dynamic time warping (DTW)
  • human activity recognition (HAR)
  • mm-wave radar sensor

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