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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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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 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.
College: Faculty of Science and Engineering
Issue: 1
Start Page: 478