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Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition

The study of human activity recognition concentrates on classifying human activities and the inference of human behavior using modern sensing technology. However, the issue of domain adaptation for inertial sensing-based human activity recognition (HAR) is still burdensome. The existing requirement...

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Detalles Bibliográficos
Autores principales: Thapa, Keshav, Seo, Yousung, Yang, Sung-Hyun, Kim, Kyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863227/
https://www.ncbi.nlm.nih.gov/pubmed/36679478
http://dx.doi.org/10.3390/s23020683
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author Thapa, Keshav
Seo, Yousung
Yang, Sung-Hyun
Kim, Kyong
author_facet Thapa, Keshav
Seo, Yousung
Yang, Sung-Hyun
Kim, Kyong
author_sort Thapa, Keshav
collection PubMed
description The study of human activity recognition concentrates on classifying human activities and the inference of human behavior using modern sensing technology. However, the issue of domain adaptation for inertial sensing-based human activity recognition (HAR) is still burdensome. The existing requirement of labeled training data for adapting such classifiers to every new person, device, or on-body location is a significant barrier to the widespread adoption of HAR-based applications, making this a challenge of high practical importance. We propose the semi-supervised HAR method to improve reconstruction and generation. It executes proper adaptation with unlabeled data without changes to a pre-trained HAR classifier. Our approach decouples VAE with adversarial learning to ensure robust classifier operation, without newly labeled training data, under changes to the individual activity and the on-body sensor position. Our proposed framework shows the empirical results using the publicly available benchmark dataset compared to state-of-art baselines, achieving competitive improvement for handling new and unlabeled activity. The result demonstrates SAA has achieved a 5% improvement in classification score compared to the existing HAR platform.
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spelling pubmed-98632272023-01-22 Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition Thapa, Keshav Seo, Yousung Yang, Sung-Hyun Kim, Kyong Sensors (Basel) Article The study of human activity recognition concentrates on classifying human activities and the inference of human behavior using modern sensing technology. However, the issue of domain adaptation for inertial sensing-based human activity recognition (HAR) is still burdensome. The existing requirement of labeled training data for adapting such classifiers to every new person, device, or on-body location is a significant barrier to the widespread adoption of HAR-based applications, making this a challenge of high practical importance. We propose the semi-supervised HAR method to improve reconstruction and generation. It executes proper adaptation with unlabeled data without changes to a pre-trained HAR classifier. Our approach decouples VAE with adversarial learning to ensure robust classifier operation, without newly labeled training data, under changes to the individual activity and the on-body sensor position. Our proposed framework shows the empirical results using the publicly available benchmark dataset compared to state-of-art baselines, achieving competitive improvement for handling new and unlabeled activity. The result demonstrates SAA has achieved a 5% improvement in classification score compared to the existing HAR platform. MDPI 2023-01-06 /pmc/articles/PMC9863227/ /pubmed/36679478 http://dx.doi.org/10.3390/s23020683 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Thapa, Keshav
Seo, Yousung
Yang, Sung-Hyun
Kim, Kyong
Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition
title Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition
title_full Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition
title_fullStr Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition
title_full_unstemmed Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition
title_short Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition
title_sort semi-supervised adversarial auto-encoder to expedite human activity recognition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863227/
https://www.ncbi.nlm.nih.gov/pubmed/36679478
http://dx.doi.org/10.3390/s23020683
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AT kimkyong semisupervisedadversarialautoencodertoexpeditehumanactivityrecognition