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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...

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Detalles Bibliográficos
Autores principales: Fan, Liufeng, Zhang, Zhan, Zhu, Biao, Zuo, Decheng, Yu, Xintong, Wang, Yiwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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