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Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication
This study has designed and developed a smart data glove based on five-channel flexible capacitive stretch sensors and a six-axis inertial measurement unit (IMU) to recognize 25 static hand gestures and ten dynamic hand gestures for amphibious communication. The five-channel flexible capacitive sens...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10673220/ https://www.ncbi.nlm.nih.gov/pubmed/38004907 http://dx.doi.org/10.3390/mi14112050 |
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author | Fan, Liufeng Zhang, Zhan Zhu, Biao Zuo, Decheng Yu, Xintong Wang, Yiwei |
author_facet | Fan, Liufeng Zhang, Zhan Zhu, Biao Zuo, Decheng Yu, Xintong Wang, Yiwei |
author_sort | Fan, Liufeng |
collection | PubMed |
description | This study has designed and developed a smart data glove based on five-channel flexible capacitive stretch sensors and a six-axis inertial measurement unit (IMU) to recognize 25 static hand gestures and ten dynamic hand gestures for amphibious communication. The five-channel flexible capacitive sensors are fabricated on a glove to capture finger motion data in order to recognize static hand gestures and integrated with six-axis IMU data to recognize dynamic gestures. This study also proposes a novel amphibious hierarchical gesture recognition (AHGR) model. This model can adaptively switch between large complex and lightweight gesture recognition models based on environmental changes to ensure gesture recognition accuracy and effectiveness. The large complex model is based on the proposed SqueezeNet-BiLSTM algorithm, specially designed for the land environment, which will use all the sensory data captured from the smart data glove to recognize dynamic gestures, achieving a recognition accuracy of 98.21%. The lightweight stochastic singular value decomposition (SVD)-optimized spectral clustering gesture recognition algorithm for underwater environments that will perform direct inference on the glove-end side can reach an accuracy of 98.35%. This study also proposes a domain separation network (DSN)-based gesture recognition transfer model that ensures a 94% recognition accuracy for new users and new glove devices. |
format | Online Article Text |
id | pubmed-10673220 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106732202023-10-31 Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication Fan, Liufeng Zhang, Zhan Zhu, Biao Zuo, Decheng Yu, Xintong Wang, Yiwei Micromachines (Basel) Article This study has designed and developed a smart data glove based on five-channel flexible capacitive stretch sensors and a six-axis inertial measurement unit (IMU) to recognize 25 static hand gestures and ten dynamic hand gestures for amphibious communication. The five-channel flexible capacitive sensors are fabricated on a glove to capture finger motion data in order to recognize static hand gestures and integrated with six-axis IMU data to recognize dynamic gestures. This study also proposes a novel amphibious hierarchical gesture recognition (AHGR) model. This model can adaptively switch between large complex and lightweight gesture recognition models based on environmental changes to ensure gesture recognition accuracy and effectiveness. The large complex model is based on the proposed SqueezeNet-BiLSTM algorithm, specially designed for the land environment, which will use all the sensory data captured from the smart data glove to recognize dynamic gestures, achieving a recognition accuracy of 98.21%. The lightweight stochastic singular value decomposition (SVD)-optimized spectral clustering gesture recognition algorithm for underwater environments that will perform direct inference on the glove-end side can reach an accuracy of 98.35%. This study also proposes a domain separation network (DSN)-based gesture recognition transfer model that ensures a 94% recognition accuracy for new users and new glove devices. MDPI 2023-10-31 /pmc/articles/PMC10673220/ /pubmed/38004907 http://dx.doi.org/10.3390/mi14112050 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 Fan, Liufeng Zhang, Zhan Zhu, Biao Zuo, Decheng Yu, Xintong Wang, Yiwei Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication |
title | Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication |
title_full | Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication |
title_fullStr | Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication |
title_full_unstemmed | Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication |
title_short | Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication |
title_sort | smart-data-glove-based gesture recognition for amphibious communication |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10673220/ https://www.ncbi.nlm.nih.gov/pubmed/38004907 http://dx.doi.org/10.3390/mi14112050 |
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