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Deep Learning Techniques for Radar-Based Continuous Human Activity Recognition
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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
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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 |
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| ISSN: | 2504-4990 |
| Published: |
MDPI AG
2023
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa64788 |
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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. |
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| 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 |