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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/4/1364 |
| _version_ | 1797396447417597952 |
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| author | Seulah Lee Yuna Choi Minchang Sung Jihyun Bae Youngjin Choi |
| author_facet | Seulah Lee Yuna Choi Minchang Sung Jihyun Bae Youngjin Choi |
| author_sort | Seulah Lee |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T00:51:24Z |
| format | Article |
| id | doaj.art-57c73b825402408ca25ed5ed03cb2478 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T00:51:24Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-57c73b825402408ca25ed5ed03cb24782023-12-11T17:09:31ZengMDPI AGSensors1424-82202021-02-01214136410.3390/s21041364A Knitted Sensing Glove for Human Hand Postures Pattern RecognitionSeulah Lee0Yuna Choi1Minchang Sung2Jihyun Bae3Youngjin Choi4Department of Electrical and Electronic Engineering, Hanyang University, Ansan 15588, KoreaDepartment of Electrical and Electronic Engineering, Hanyang University, Ansan 15588, KoreaDepartment of Electrical and Electronic Engineering, Hanyang University, Ansan 15588, KoreaHuman-Tech Convergence Program, Department of Clothing and Textiles, Hanyang University, Seoul 04763, KoreaDepartment of Electrical and Electronic Engineering, Hanyang University, Ansan 15588, KoreaIn 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.https://www.mdpi.com/1424-8220/21/4/1364knitted sensorwearable strain sensordata glovefabric sensorpattern recognition |
| spellingShingle | Seulah Lee Yuna Choi Minchang Sung Jihyun Bae Youngjin Choi A Knitted Sensing Glove for Human Hand Postures Pattern Recognition Sensors knitted sensor wearable strain sensor data glove fabric sensor 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 | knitted sensor wearable strain sensor data glove fabric sensor pattern recognition |
| url | https://www.mdpi.com/1424-8220/21/4/1364 |
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