Development of a prosthetic thumb prototype based on thumb-tip force estimation /
Finger amputation is increasing every year due to accidents, diseases and congenital amputation. The loss of thumb could limit the proper hand functions and thus affect human daily activities. As a solution, a prosthetic thumb can be worn as a replacement to the real thumb. Natural control of the pr...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
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Kuala Lumpur:
Kulliyyah of Engineering, International Islamic University Malaysia,
2013
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| Subjects: | |
| Online Access: | http://studentrepo.iium.edu.my/handle/123456789/4556 |
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| Summary: | Finger amputation is increasing every year due to accidents, diseases and congenital amputation. The loss of thumb could limit the proper hand functions and thus affect human daily activities. As a solution, a prosthetic thumb can be worn as a replacement to the real thumb. Natural control of the prosthetic device is desired and can be achieved by controlling the movement and force based on the real thumb model. Real thumb operates by muscles through muscle contraction. During contraction, muscle fibres inside the muscles are excited and electrical signals known as Electromyogram (EMG) signals are generated. These signals can be measured non-invasively using surface electrodes and can be used to control the prosthetic thumb. In this research, the EMG signals are measured simultaneously with the thumb-tip forces at different joint angles from five subjects. The considered muscles are four thumb intrinsic muscles that are located at the outermost layer namely First Dorsal Interosseus (FDI), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and Adductor Pollicis (AP). The collected signals from the muscles are extracted in order to establish the relationship between EMG signals and thumb-tip forces at different joint angles. The model of the relationships is developed by using Artificial Neural Network (ANN) with the EMG signals are set as the inputs and the thumb-tip force and joint angles are set as the outputs. The performances of the established network are evaluated by calculating the Root Mean Square Error (RMSE) between the actual outputs and the estimated outputs. The ANN with the smallest RMSE is included in the system that controls the prosthetic thumb movement and force. This network is function to extract force and angle information from EMG signals for the prosthetic thumb prototype to move and exert force. The performance of the prosthetic thumb movement and thumb-tip force shows that the applied technique is suitable for developing a prosthetic thumb prototype. |
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| Physical Description: | xvi, 114 leaves : ill. ; 30cm. |
| Bibliography: | Includes bibliographical references (leaves 108-113). |