Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine
This paper proposes an object classification method using a flexion glove and machine learning. The classification is performed based on the information obtained from a single grasp on a target object. The flexion glove is developed with five flex sensors mounted on five finger sleeves, and is used...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-02-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/4/1461 |
| _version_ | 1827588734474780672 |
|---|---|
| author | Shun-Hsin Yu Jen-Shuo Chang Chia-Hung Dylan Tsai |
| author_facet | Shun-Hsin Yu Jen-Shuo Chang Chia-Hung Dylan Tsai |
| author_sort | Shun-Hsin Yu |
| collection | DOAJ |
| description | This paper proposes an object classification method using a flexion glove and machine learning. The classification is performed based on the information obtained from a single grasp on a target object. The flexion glove is developed with five flex sensors mounted on five finger sleeves, and is used for measuring the flexion of individual fingers while grasping an object. Flexion signals are divided into three phases, and they are the phases of picking, holding and releasing, respectively. Grasping features are extracted from the phase of holding for training the support vector machine. Two sets of objects are prepared for the classification test. One is printed-object set and the other is daily-life object set. The printed-object set is for investigating the patterns of grasping with specified shape and size, while the daily-life object set includes nine objects randomly chosen from daily life for demonstrating that the proposed method can be used to identify a wide range of objects. According to the results, the accuracy of the classifications are achieved 95.56% and 88.89% for the sets of printed objects and daily-life objects, respectively. A flexion glove which can perform object classification is successfully developed in this work and is aimed at potential grasp-to-see applications, such as visual impairment aid and recognition in dark space. |
| first_indexed | 2024-03-09T00:42:53Z |
| format | Article |
| id | doaj.art-73ee91d9e24b4e68b22a61c96bfa801a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T00:42:53Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-73ee91d9e24b4e68b22a61c96bfa801a2023-12-11T17:43:57ZengMDPI AGSensors1424-82202021-02-01214146110.3390/s21041461Grasp to See—Object Classification Using Flexion Glove with Support Vector MachineShun-Hsin Yu0Jen-Shuo Chang1Chia-Hung Dylan Tsai2Graduate Degree Program of Robotics, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanDepartment of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanGraduate Degree Program of Robotics, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanThis paper proposes an object classification method using a flexion glove and machine learning. The classification is performed based on the information obtained from a single grasp on a target object. The flexion glove is developed with five flex sensors mounted on five finger sleeves, and is used for measuring the flexion of individual fingers while grasping an object. Flexion signals are divided into three phases, and they are the phases of picking, holding and releasing, respectively. Grasping features are extracted from the phase of holding for training the support vector machine. Two sets of objects are prepared for the classification test. One is printed-object set and the other is daily-life object set. The printed-object set is for investigating the patterns of grasping with specified shape and size, while the daily-life object set includes nine objects randomly chosen from daily life for demonstrating that the proposed method can be used to identify a wide range of objects. According to the results, the accuracy of the classifications are achieved 95.56% and 88.89% for the sets of printed objects and daily-life objects, respectively. A flexion glove which can perform object classification is successfully developed in this work and is aimed at potential grasp-to-see applications, such as visual impairment aid and recognition in dark space.https://www.mdpi.com/1424-8220/21/4/1461graspingflex sensingobject classificationmachine learning |
| spellingShingle | Shun-Hsin Yu Jen-Shuo Chang Chia-Hung Dylan Tsai Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine Sensors grasping flex sensing object classification machine learning |
| title | Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine |
| title_full | Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine |
| title_fullStr | Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine |
| title_full_unstemmed | Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine |
| title_short | Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine |
| title_sort | grasp to see object classification using flexion glove with support vector machine |
| topic | grasping flex sensing object classification machine learning |
| url | https://www.mdpi.com/1424-8220/21/4/1461 |
| work_keys_str_mv | AT shunhsinyu grasptoseeobjectclassificationusingflexionglovewithsupportvectormachine AT jenshuochang grasptoseeobjectclassificationusingflexionglovewithsupportvectormachine AT chiahungdylantsai grasptoseeobjectclassificationusingflexionglovewithsupportvectormachine |