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A Knitted Sensing Glove for Human Hand Postures Pattern Recognition
In recent years, flexible sensors for data gloves have been developed that aim to achieve excellent wearability, but they are associated with difficulties due to the complicated manufacturing and embedding into the glove. This study proposes a knitted glove integrated with strain sensors for pattern...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7919032/ https://www.ncbi.nlm.nih.gov/pubmed/33671966 http://dx.doi.org/10.3390/s21041364 |
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author | Lee, Seulah Choi, Yuna Sung, Minchang Bae, Jihyun Choi, Youngjin |
author_facet | Lee, Seulah Choi, Yuna Sung, Minchang Bae, Jihyun Choi, Youngjin |
author_sort | Lee, Seulah |
collection | PubMed |
description | In recent years, flexible sensors for data gloves have been developed that aim to achieve excellent wearability, but they are associated with difficulties due to the complicated manufacturing and embedding into the glove. This study proposes a knitted glove integrated with strain sensors for pattern recognition of hand postures. The proposed sensing glove is fabricated at all once by a knitting technique without sewing and bonding, which is composed of strain sensors knitted with conductive yarn and a glove body with non-conductive yarn. To verify the performance of the developed glove, electrical resistance variations were measured according to the flexed angle and speed. These data showed different values depending on the speed or angle of movements. We carried out experiments on hand postures pattern recognition for the practicability verification of the knitted sensing glove. For this purpose, 10 able-bodied subjects participated in the recognition experiments on 10 target hand postures. The average classification accuracy of 10 subjects reached 94.17% when their own data were used. The accuracy of up to 97.1% was achieved in the case of grasp posture among 10 target postures. When all mixed data from 10 subjects were utilized for pattern recognition, the average classification expressed by the confusion matrix arrived at 89.5%. Therefore, the comprehensive experimental results demonstrated the effectiveness of the knitted sensing gloves. In addition, it is expected to reduce the cost through a simple manufacturing process of the knitted sensing glove. |
format | Online Article Text |
id | pubmed-7919032 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79190322021-03-02 A Knitted Sensing Glove for Human Hand Postures Pattern Recognition Lee, Seulah Choi, Yuna Sung, Minchang Bae, Jihyun Choi, Youngjin Sensors (Basel) Article In recent years, flexible sensors for data gloves have been developed that aim to achieve excellent wearability, but they are associated with difficulties due to the complicated manufacturing and embedding into the glove. This study proposes a knitted glove integrated with strain sensors for pattern recognition of hand postures. The proposed sensing glove is fabricated at all once by a knitting technique without sewing and bonding, which is composed of strain sensors knitted with conductive yarn and a glove body with non-conductive yarn. To verify the performance of the developed glove, electrical resistance variations were measured according to the flexed angle and speed. These data showed different values depending on the speed or angle of movements. We carried out experiments on hand postures pattern recognition for the practicability verification of the knitted sensing glove. For this purpose, 10 able-bodied subjects participated in the recognition experiments on 10 target hand postures. The average classification accuracy of 10 subjects reached 94.17% when their own data were used. The accuracy of up to 97.1% was achieved in the case of grasp posture among 10 target postures. When all mixed data from 10 subjects were utilized for pattern recognition, the average classification expressed by the confusion matrix arrived at 89.5%. Therefore, the comprehensive experimental results demonstrated the effectiveness of the knitted sensing gloves. In addition, it is expected to reduce the cost through a simple manufacturing process of the knitted sensing glove. MDPI 2021-02-15 /pmc/articles/PMC7919032/ /pubmed/33671966 http://dx.doi.org/10.3390/s21041364 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lee, Seulah Choi, Yuna Sung, Minchang Bae, Jihyun Choi, Youngjin A Knitted Sensing Glove for Human Hand Postures Pattern Recognition |
title | A Knitted Sensing Glove for Human Hand Postures Pattern Recognition |
title_full | A Knitted Sensing Glove for Human Hand Postures Pattern Recognition |
title_fullStr | A Knitted Sensing Glove for Human Hand Postures Pattern Recognition |
title_full_unstemmed | A Knitted Sensing Glove for Human Hand Postures Pattern Recognition |
title_short | A Knitted Sensing Glove for Human Hand Postures Pattern Recognition |
title_sort | knitted sensing glove for human hand postures pattern recognition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7919032/ https://www.ncbi.nlm.nih.gov/pubmed/33671966 http://dx.doi.org/10.3390/s21041364 |
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