Deep Learning Techniques for Radar-Based Continuous Human Activity Recognition

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...

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Published in:	Machine learning and knowledge extraction Vol. 5; no. 4; pp. 1493 - 1518
Main Authors:	Mehta, Ruchita, Sharifzadeh, Sara, Palade, Vasile, Tan, Bo, Daneshkhah, Alireza, Karayaneva, Yordanka
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.12.2023
Subjects:	Activities of daily living Algorithms Annotations Artificial neural networks Coding convolutional variational autoencoder (CVAE) Data analysis Data collection Deep learning deep neural networks (DNNs) Doppler radar dynamic time warping (DTW) Equalization Feature extraction Frames (data processing) Human activity recognition human activity recognition (HAR) Machine learning Millimeter waves mm-wave radar sensor Neural networks Object recognition (Computers) Older people Pattern recognition Principal components analysis Privacy Radar systems Sensors Wearable computers United Kingdom
ISSN:	2504-4990, 2504-4990
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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.
AbstractList	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.
Audience	Academic
Author	Daneshkhah, Alireza Sharifzadeh, Sara Palade, Vasile Mehta, Ruchita Karayaneva, Yordanka Tan, Bo
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SubjectTerms	Activities of daily living Algorithms Annotations Artificial neural networks Coding convolutional variational autoencoder (CVAE) Data analysis Data collection Deep learning deep neural networks (DNNs) Doppler radar dynamic time warping (DTW) Equalization Feature extraction Frames (data processing) Human activity recognition human activity recognition (HAR) Machine learning Millimeter waves mm-wave radar sensor Neural networks Object recognition (Computers) Older people Pattern recognition Principal components analysis Privacy Radar systems Sensors Wearable computers
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Title	Deep Learning Techniques for Radar-Based Continuous Human Activity Recognition
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