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A Novel Deep Neural Network Method for HAR-Based Team Training Using Body-Worn Inertial Sensors
Human activity recognition (HAR) became a challenging issue in recent years. In this paper, we propose a novel approach to tackle indistinguishable activity recognition based on human wearable sensors. Generally speaking, vision-based solutions struggle with low illumination environments and partial...
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/PMC9658685/ https://www.ncbi.nlm.nih.gov/pubmed/36366202 http://dx.doi.org/10.3390/s22218507 |
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author | Fan, Yun-Chieh Tseng, Yu-Hsuan Wen, Chih-Yu |
author_facet | Fan, Yun-Chieh Tseng, Yu-Hsuan Wen, Chih-Yu |
author_sort | Fan, Yun-Chieh |
collection | PubMed |
description | Human activity recognition (HAR) became a challenging issue in recent years. In this paper, we propose a novel approach to tackle indistinguishable activity recognition based on human wearable sensors. Generally speaking, vision-based solutions struggle with low illumination environments and partial occlusion problems. In contrast, wearable inertial sensors can tackle this problem and avoid revealing personal privacy. We address the issue by building a multistage deep neural network framework that interprets accelerometer, gyroscope, and magnetometer data that provide useful information of human activities. Initially, the stage of variational autoencoders (VAE) can extract the crucial information from raw data of inertial measurement units (IMUs). Furthermore, the stage of generative adversarial networks (GANs) can generate more realistic human activities. Finally, the transfer learning method is applied to enhance the performance of the target domain, which builds a robust and effective model to recognize human activities. |
format | Online Article Text |
id | pubmed-9658685 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96586852022-11-15 A Novel Deep Neural Network Method for HAR-Based Team Training Using Body-Worn Inertial Sensors Fan, Yun-Chieh Tseng, Yu-Hsuan Wen, Chih-Yu Sensors (Basel) Article Human activity recognition (HAR) became a challenging issue in recent years. In this paper, we propose a novel approach to tackle indistinguishable activity recognition based on human wearable sensors. Generally speaking, vision-based solutions struggle with low illumination environments and partial occlusion problems. In contrast, wearable inertial sensors can tackle this problem and avoid revealing personal privacy. We address the issue by building a multistage deep neural network framework that interprets accelerometer, gyroscope, and magnetometer data that provide useful information of human activities. Initially, the stage of variational autoencoders (VAE) can extract the crucial information from raw data of inertial measurement units (IMUs). Furthermore, the stage of generative adversarial networks (GANs) can generate more realistic human activities. Finally, the transfer learning method is applied to enhance the performance of the target domain, which builds a robust and effective model to recognize human activities. MDPI 2022-11-04 /pmc/articles/PMC9658685/ /pubmed/36366202 http://dx.doi.org/10.3390/s22218507 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 Fan, Yun-Chieh Tseng, Yu-Hsuan Wen, Chih-Yu A Novel Deep Neural Network Method for HAR-Based Team Training Using Body-Worn Inertial Sensors |
title | A Novel Deep Neural Network Method for HAR-Based Team Training Using Body-Worn Inertial Sensors |
title_full | A Novel Deep Neural Network Method for HAR-Based Team Training Using Body-Worn Inertial Sensors |
title_fullStr | A Novel Deep Neural Network Method for HAR-Based Team Training Using Body-Worn Inertial Sensors |
title_full_unstemmed | A Novel Deep Neural Network Method for HAR-Based Team Training Using Body-Worn Inertial Sensors |
title_short | A Novel Deep Neural Network Method for HAR-Based Team Training Using Body-Worn Inertial Sensors |
title_sort | novel deep neural network method for har-based team training using body-worn inertial sensors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9658685/ https://www.ncbi.nlm.nih.gov/pubmed/36366202 http://dx.doi.org/10.3390/s22218507 |
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