Journal article 395 views 45 downloads
Deep Learning Techniques for Radar-Based Continuous Human Activity Recognition
,
Sara Sharifzadeh ,
Vasile Palade ,
Bo Tan ,
Alireza Daneshkhah ,
Yordanka Karayaneva
Machine Learning and Knowledge Extraction, Volume: 5, Issue: 4, Pages: 1493 - 1518
Swansea University Author:
Sara Sharifzadeh
-
PDF | Version of Record
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. Distributed under the terms of a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Download (4.19MB)
DOI (Published version): 10.3390/make5040075
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...
| Published in: | Machine Learning and Knowledge Extraction |
|---|---|
| ISSN: | 2504-4990 |
| Published: |
MDPI AG
2023
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa64788 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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. |
|---|---|
| Keywords: |
Human activity recognition (HAR), dynamic time warping (DTW), convolutional variational autoencoder (CVAE), mm-wave radar sensor, deep neural networks (DNNs) |
| College: |
Faculty of Science and Engineering |
| Funders: |
Coventry University |
| Issue: |
4 |
| Start Page: |
1493 |
| End Page: |
1518 |