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Continuous Human Activity Recognition using Radar Imagery and Dynamic Time Warping

Ruchita Mehta, Vasile Palade, Sara Sharifzadeh Orcid Logo, Bo Tan, Yordanka Karayaneva

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

Swansea University Author: Sara Sharifzadeh Orcid Logo

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DOI (Published version): 10.1109/icmla55696.2022.00076

Abstract

Remote Human Activity Recognition (HAR) in a private residential area has a beneficial influence on the elderly population's life, since this group of people require regular monitoring of health conditions. This paper addresses the problem of continuous detection of daily human activities using...

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Published in: 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
Published: IEEE 2022
Online Access: http://dx.doi.org/10.1109/icmla55696.2022.00076
URI: https://cronfa.swan.ac.uk/Record/cronfa63203
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first_indexed 2023-04-25T11:00:45Z
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spelling v2 63203 2023-04-19 Continuous Human Activity Recognition using Radar Imagery and Dynamic Time Warping a4e15f304398ecee3f28c7faec69c1b0 0000-0003-4621-2917 Sara Sharifzadeh Sara Sharifzadeh true false 2023-04-19 SCS Remote Human Activity Recognition (HAR) in a private residential area has a beneficial influence on the elderly population's life, since this group of people require regular monitoring of health conditions. This paper addresses the problem of continuous detection of daily human activities using mm-wave Doppler radar. Unlike most previous research, this work records the data in terms of continuous series of activities rather than individual activities. These series of activities are similar to real-life activity patterns. The Dynamic Time Warping (DTW) algorithm is used for the detection of human activities in the recorded time series of data and compared to other time-series classification methods. DTW requires less amount of labelled data. The input for DTW was provided using three strategies, and the obtained results were compared against each other. The first approach uses the pixel-level data of frames (named 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. Results demonstrates the superiority of the Sup-EnLevel features over UnSup-EnLevel and UnSup-PLevel strategies. However, the performance of the UnSup-PLevel strategy worked surprisingly well without using annotations. Conference Paper/Proceeding/Abstract 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) IEEE 1 12 2022 2022-12-01 10.1109/icmla55696.2022.00076 http://dx.doi.org/10.1109/icmla55696.2022.00076 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2023-04-25T16:15:14.2630364 2023-04-19T13:58:41.9166443 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Ruchita Mehta 1 Vasile Palade 2 Sara Sharifzadeh 0000-0003-4621-2917 3 Bo Tan 4 Yordanka Karayaneva 5
title Continuous Human Activity Recognition using Radar Imagery and Dynamic Time Warping
spellingShingle Continuous Human Activity Recognition using Radar Imagery and Dynamic Time Warping
Sara Sharifzadeh
title_short Continuous Human Activity Recognition using Radar Imagery and Dynamic Time Warping
title_full Continuous Human Activity Recognition using Radar Imagery and Dynamic Time Warping
title_fullStr Continuous Human Activity Recognition using Radar Imagery and Dynamic Time Warping
title_full_unstemmed Continuous Human Activity Recognition using Radar Imagery and Dynamic Time Warping
title_sort Continuous Human Activity Recognition using Radar Imagery and Dynamic Time Warping
author_id_str_mv a4e15f304398ecee3f28c7faec69c1b0
author_id_fullname_str_mv a4e15f304398ecee3f28c7faec69c1b0_***_Sara Sharifzadeh
author Sara Sharifzadeh
author2 Ruchita Mehta
Vasile Palade
Sara Sharifzadeh
Bo Tan
Yordanka Karayaneva
format Conference Paper/Proceeding/Abstract
container_title 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
publishDate 2022
institution Swansea University
doi_str_mv 10.1109/icmla55696.2022.00076
publisher IEEE
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.1109/icmla55696.2022.00076
document_store_str 0
active_str 0
description Remote Human Activity Recognition (HAR) in a private residential area has a beneficial influence on the elderly population's life, since this group of people require regular monitoring of health conditions. This paper addresses the problem of continuous detection of daily human activities using mm-wave Doppler radar. Unlike most previous research, this work records the data in terms of continuous series of activities rather than individual activities. These series of activities are similar to real-life activity patterns. The Dynamic Time Warping (DTW) algorithm is used for the detection of human activities in the recorded time series of data and compared to other time-series classification methods. DTW requires less amount of labelled data. The input for DTW was provided using three strategies, and the obtained results were compared against each other. The first approach uses the pixel-level data of frames (named 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. Results demonstrates the superiority of the Sup-EnLevel features over UnSup-EnLevel and UnSup-PLevel strategies. However, the performance of the UnSup-PLevel strategy worked surprisingly well without using annotations.
published_date 2022-12-01T16:15:12Z
_version_ 1764161709937786880
score 11.016593

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