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Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition
Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world envir...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572241/ https://www.ncbi.nlm.nih.gov/pubmed/36236427 http://dx.doi.org/10.3390/s22197324 |
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author | Bento, Nuno Rebelo, Joana Barandas, Marília Carreiro, André V. Campagner, Andrea Cabitza, Federico Gamboa, Hugo |
author_facet | Bento, Nuno Rebelo, Joana Barandas, Marília Carreiro, André V. Campagner, Andrea Cabitza, Federico Gamboa, Hugo |
author_sort | Bento, Nuno |
collection | PubMed |
description | Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains. |
format | Online Article Text |
id | pubmed-9572241 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95722412022-10-17 Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition Bento, Nuno Rebelo, Joana Barandas, Marília Carreiro, André V. Campagner, Andrea Cabitza, Federico Gamboa, Hugo Sensors (Basel) Article Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains. MDPI 2022-09-27 /pmc/articles/PMC9572241/ /pubmed/36236427 http://dx.doi.org/10.3390/s22197324 Text en © 2022 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 Bento, Nuno Rebelo, Joana Barandas, Marília Carreiro, André V. Campagner, Andrea Cabitza, Federico Gamboa, Hugo Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition |
title | Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition |
title_full | Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition |
title_fullStr | Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition |
title_full_unstemmed | Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition |
title_short | Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition |
title_sort | comparing handcrafted features and deep neural representations for domain generalization in human activity recognition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572241/ https://www.ncbi.nlm.nih.gov/pubmed/36236427 http://dx.doi.org/10.3390/s22197324 |
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