Abstract
Myoelectric control has a key role in human-machine interface applications such as orthosis control and teleoperation. Myoelectric signals are bio signals that are detectable from surface of the skin, and contain useful information about user's moving intention. This paper presents a methodology to estimate elbow joint angle from muscle's data using neural network (NN). Proposed methodology can be expanded to estimate any joint angle by recording muscle activities concerning with the joint. In addition, Nonlinear Auto-Regressive eXogenous-NN (NARX-NN) model is selected to estimate joint angle. Several data sets are recorded and processed to train and test the NN. The trained network is used to predict elbow angles for individual data sets. Results show that trained network can estimate joint angle with an acceptable performance.
Original language | English |
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Title of host publication | International Conference on Robotics and Mechatronics, ICROM 2015 |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 444-449 |
Number of pages | 6 |
ISBN (Electronic) | 9781467372343 |
DOIs | |
Publication status | Published - 28 Dec 2015 |
Externally published | Yes |
Event | RSI/ISM International Conference on Robotics and Mechatronics 2015 - Tehran, Iran Duration: 7 Oct 2015 → 9 Oct 2015 Conference number: 3rd https://ieeexplore.ieee.org/xpl/conhome/7352843/proceeding (Proceedings) |
Conference
Conference | RSI/ISM International Conference on Robotics and Mechatronics 2015 |
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Abbreviated title | ICROM 2015 |
Country/Territory | Iran |
City | Tehran |
Period | 7/10/15 → 9/10/15 |
Internet address |
Keywords
- EMG
- Myoelectric control
- NARX
- Neural network
- Rehabilitation