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Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data

Yordanka Karayaneva, Sara Sharifzadeh Orcid Logo, Yanguo Jing, Bo Tan Orcid Logo

Sensors, Volume: 23, Issue: 1, Start page: 478

Swansea University Author: Sara Sharifzadeh Orcid Logo

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DOI (Published version): 10.3390/s23010478

Abstract

This paper explores the feasibility of using low-resolution infrared (LRIR) image streams for human activity recognition (HAR) with potential application in e-healthcare. Two datasets based on synchronized multichannel LRIR sensors systems are considered for a comprehensive study about optimal data...

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Published in: Sensors
ISSN: 1424-8220
Published: MDPI AG 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa62398
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spelling 2024-07-11T15:08:47.6259458 v2 62398 2023-01-23 Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data a4e15f304398ecee3f28c7faec69c1b0 0000-0003-4621-2917 Sara Sharifzadeh Sara Sharifzadeh true false 2023-01-23 MACS This paper explores the feasibility of using low-resolution infrared (LRIR) image streams for human activity recognition (HAR) with potential application in e-healthcare. Two datasets based on synchronized multichannel LRIR sensors systems are considered for a comprehensive study about optimal data acquisition. A novel noise reduction technique is proposed for alleviating the effects of horizontal and vertical periodic noise in the 2D spatiotemporal activity profiles created by vectorizing and concatenating the LRIR frames. Two main analysis strategies are explored for HAR, including (1) manual feature extraction using texture-based and orthogonal-transformation-based techniques, followed by classification using support vector machine (SVM), random forest (RF), k-nearest neighbor (k-NN), and logistic regression (LR), and (2) deep neural network (DNN) strategy based on a convolutional long short-term memory (LSTM). The proposed periodic noise reduction technique showcases an increase of up to 14.15% using different models. In addition, for the first time, the optimum number of sensors, sensor layout, and distance to subjects are studied, indicating the optimum results based on a single side sensor at a close distance. Reasonable accuracies are achieved in the case of sensor displacement and robustness in detection of multiple subjects. Furthermore, the models show suitability for data collected in different environments. Journal Article Sensors 23 1 478 MDPI AG 1424-8220 human activity recognition (HAR); infrared sensors; noise reduction; feature extraction; classification; AI-enabled healthcare 2 1 2023 2023-01-02 10.3390/s23010478 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University This research was funded by the Data Driven Research and Innovation (DDRI) grant at Coventry University, UK. 2024-07-11T15:08:47.6259458 2023-01-23T10:38:54.7775192 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Yordanka Karayaneva 1 Sara Sharifzadeh 0000-0003-4621-2917 2 Yanguo Jing 3 Bo Tan 0000-0002-6855-6270 4 62398__26354__28e598d7ad0b468fb9f20d832ed1b393.pdf 62398.pdf 2023-01-23T10:43:51.4378940 Output 2637802 application/pdf Version of Record true This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/ 162 true https://ieee-dataport.org/documents/infrared-human-activity-recognition-dataset-coventry-2018
title Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data
spellingShingle Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data
Sara Sharifzadeh
title_short Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data
title_full Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data
title_fullStr Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data
title_full_unstemmed Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data
title_sort Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data
author_id_str_mv a4e15f304398ecee3f28c7faec69c1b0
author_id_fullname_str_mv a4e15f304398ecee3f28c7faec69c1b0_***_Sara Sharifzadeh
author Sara Sharifzadeh
author2 Yordanka Karayaneva
Sara Sharifzadeh
Yanguo Jing
Bo Tan
format Journal article
container_title Sensors
container_volume 23
container_issue 1
container_start_page 478
publishDate 2023
institution Swansea University
issn 1424-8220
doi_str_mv 10.3390/s23010478
publisher MDPI AG
college_str Faculty of Science and Engineering
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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
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description This paper explores the feasibility of using low-resolution infrared (LRIR) image streams for human activity recognition (HAR) with potential application in e-healthcare. Two datasets based on synchronized multichannel LRIR sensors systems are considered for a comprehensive study about optimal data acquisition. A novel noise reduction technique is proposed for alleviating the effects of horizontal and vertical periodic noise in the 2D spatiotemporal activity profiles created by vectorizing and concatenating the LRIR frames. Two main analysis strategies are explored for HAR, including (1) manual feature extraction using texture-based and orthogonal-transformation-based techniques, followed by classification using support vector machine (SVM), random forest (RF), k-nearest neighbor (k-NN), and logistic regression (LR), and (2) deep neural network (DNN) strategy based on a convolutional long short-term memory (LSTM). The proposed periodic noise reduction technique showcases an increase of up to 14.15% using different models. In addition, for the first time, the optimum number of sensors, sensor layout, and distance to subjects are studied, indicating the optimum results based on a single side sensor at a close distance. Reasonable accuracies are achieved in the case of sensor displacement and robustness in detection of multiple subjects. Furthermore, the models show suitability for data collected in different environments.
published_date 2023-01-02T08:18:29Z
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score 11.04748

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