Relationship investigation of handgrip forces with varied wrist angles using forearm EMG for bionic hand
Extracting hand grip force and wrist angle information from forearm electromyogram (EMG) signals is useful to be used as an inputs for the control of cybernetic prostheses or robotic hand. The information relating handgrip force and wrist position to forearm muscle activity is important as control a...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/20911/1/Relationship%20investigation%20of%20handgrip%20forces%20with%20varied%20wrist%20angles%20using%20forearm%20EMG%20for%20bionic%20hand.pdf http://eprints.utem.edu.my/id/eprint/20911/2/Relationship%20investigation%20of%20handgrip%20forces%20with%20varied%20wrist%20angles%20using%20forearm%20EMG%20for%20bionic%20hand.pdf |
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| Summary: | Extracting hand grip force and wrist angle information from forearm electromyogram (EMG) signals is useful to be used as an inputs for the control of cybernetic prostheses or robotic hand. The information relating handgrip force and wrist position to forearm muscle activity is important as control algorithm for controlling the prostheses or robotic hand gripping force. By investigating the relationship between forearm EMG and hand grip force/wrist angles, the prostheses or robotic hand can be controlled in a manner that is customized to an amputee's intent. In this research study, a signal processing system which consists of an electronic conditioning circuit to measure and process raw EMG signals into linear enveloped EMG signal and software to record and process the EMG signals were developed. Each circuit development stage is described in detail so that this research can be easily produced by others for future work and improvements. Experimental training and testing datasets from five subjects were collected to investigate the relationship between forearm EMG,hand grip force and wrist angle simultaneously. The wrist angles set for this research is 60,90° and 120 ° while the forces is set at 5%,15%,25% and 35%MVC.At the beginning, 100%/MVC were done by each subjects for the normalization of EMG signal Neural Network were used to represents the relationship and to estimate handgrip force and wrist angle from the EMG signal. The performance of the networks were indicated by Mean Square Error (MSE) and Mean Absolute Error (MAE) values. The results from neural network training shows good accuracy with low MSE (<= 0.0000001) and MAE(<0.2) value. The data obtained from
the experiment has been analyzed and is useful, low-cost method to control a prostheses or robotic hand. |
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