Grasping Force Controlling by Slip Detection for Specific Artificial Hand (ottobock 8E37)
This paper presents a theoretical and experimental study to control grasping force of specific artificial hand (Otto Bock 8E37), which it uses by amputees. The hand has two rigid fingers actuated by a DC motor through a multi-gears system. The aim of this work is to give the amputees a feeling of sl...
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| Format: | Article |
| Language: | English |
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Unviversity of Technology- Iraq
2018-09-01
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| Series: | Engineering and Technology Journal |
| Subjects: | |
| Online Access: | https://etj.uotechnology.edu.iq/article_175282_0f7ed0bc8926e8d038c742fe10d2f136.pdf |
| _version_ | 1797325998357741568 |
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| author | MOFAQ TWFEQ Ihsan Baqer Asaad Abdulsahib |
| author_facet | MOFAQ TWFEQ Ihsan Baqer Asaad Abdulsahib |
| author_sort | MOFAQ TWFEQ |
| collection | DOAJ |
| description | This paper presents a theoretical and experimental study to control grasping force of specific artificial hand (Otto Bock 8E37), which it uses by amputees. The hand has two rigid fingers actuated by a DC motor through a multi-gears system. The aim of this work is to give the amputees a feeling of slipping while the hand grasping an object. The mathematical model has been derived to simulate the hand mechanism and analyze the generated signal of contact force between fingertip and the grasped object through a slippage phenomenon. The experimental work consisted of modifying the artificial hand design to aid load cell mounting process in order to measure the grasping force indirectly, then acquiring the measured signal to the PC. An artificial neural network (ANN) was trained on the patterns of the force signals. These patterns were prepared by using force sensors with modified design of the artificial hand for detecting the slippage of the different shapes grasped object. The Neural Network training results have been evaluated and discussed under different conditions, which affect the controller operation such as network error, classification percentage and the response time delay. |
| first_indexed | 2024-03-08T06:17:17Z |
| format | Article |
| id | doaj.art-38d3c5fc36194cfa9dc2463e44ccddb7 |
| institution | Directory Open Access Journal |
| issn | 1681-6900 2412-0758 |
| language | English |
| last_indexed | 2024-03-08T06:17:17Z |
| publishDate | 2018-09-01 |
| publisher | Unviversity of Technology- Iraq |
| record_format | Article |
| series | Engineering and Technology Journal |
| spelling | doaj.art-38d3c5fc36194cfa9dc2463e44ccddb72024-02-04T17:16:31ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582018-09-01369A97998410.30684/etj.36.9A.6175282Grasping Force Controlling by Slip Detection for Specific Artificial Hand (ottobock 8E37)MOFAQ TWFEQ0Ihsan Baqer1Asaad Abdulsahib2Mechanical Engineering Department University of Technology Baghdad, IraqMechanical Engineering Department University of Technology Baghdad, IraqAl-qadisiyah, IraqThis paper presents a theoretical and experimental study to control grasping force of specific artificial hand (Otto Bock 8E37), which it uses by amputees. The hand has two rigid fingers actuated by a DC motor through a multi-gears system. The aim of this work is to give the amputees a feeling of slipping while the hand grasping an object. The mathematical model has been derived to simulate the hand mechanism and analyze the generated signal of contact force between fingertip and the grasped object through a slippage phenomenon. The experimental work consisted of modifying the artificial hand design to aid load cell mounting process in order to measure the grasping force indirectly, then acquiring the measured signal to the PC. An artificial neural network (ANN) was trained on the patterns of the force signals. These patterns were prepared by using force sensors with modified design of the artificial hand for detecting the slippage of the different shapes grasped object. The Neural Network training results have been evaluated and discussed under different conditions, which affect the controller operation such as network error, classification percentage and the response time delay.https://etj.uotechnology.edu.iq/article_175282_0f7ed0bc8926e8d038c742fe10d2f136.pdfartificial handslip detectiongrasping forcegrasping control |
| spellingShingle | MOFAQ TWFEQ Ihsan Baqer Asaad Abdulsahib Grasping Force Controlling by Slip Detection for Specific Artificial Hand (ottobock 8E37) Engineering and Technology Journal artificial hand slip detection grasping force grasping control |
| title | Grasping Force Controlling by Slip Detection for Specific Artificial Hand (ottobock 8E37) |
| title_full | Grasping Force Controlling by Slip Detection for Specific Artificial Hand (ottobock 8E37) |
| title_fullStr | Grasping Force Controlling by Slip Detection for Specific Artificial Hand (ottobock 8E37) |
| title_full_unstemmed | Grasping Force Controlling by Slip Detection for Specific Artificial Hand (ottobock 8E37) |
| title_short | Grasping Force Controlling by Slip Detection for Specific Artificial Hand (ottobock 8E37) |
| title_sort | grasping force controlling by slip detection for specific artificial hand ottobock 8e37 |
| topic | artificial hand slip detection grasping force grasping control |
| url | https://etj.uotechnology.edu.iq/article_175282_0f7ed0bc8926e8d038c742fe10d2f136.pdf |
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