<p>Prostheses are artificial body parts that can be used by amputees. Myoelectric prostheses are controlled by so-called surface electromyograms (sEMG) that are acquired on the skin surface of the residual limb. A well-known problem in myoelectric prostheses is motion artifacts, these artifacts cause unwanted behaviour of the prosthesis. The purpose of this study is to try to cancel the effect motion artifacts have on myoelectric prosthesis control, in order to avoid unsolicited prosthesis behaviour. The subject of myoelectric prostheses and motion artifacts are outlined in this study, together with the development and characterisation of a sensor that can do simultaneous measurements of sEMG and contact forces between a surface electrode and the skin. A protocol has been developed for the recording of the different signals in a laboratory. Suitable data sets have been recorded from one test subject, and signal processing and pattern recognition methods have been applied on these data sets to generate muscle force estimates. The pattern recognition methods were linear and quadratic mapping functions, and multi-layer perceptron network. To achieve better force estimates when motion artifacts are presence, signals from FSRs are taken into consideration together with sEMG signals. A qualitative comparison reveals obvious improvements for the sEMG sensor when FSR measurement is included. The system presently undergoes quantitative assessment of static and dynamic performance. The final step will hopefully be to integrate FSRs in a real prosthesis.</p>
| Identifer | oai:union.ndltd.org:UPSALLA/oai:DiVA.org:ntnu-9710 |
| Date | January 2008 |
| Creators | Sæther, Marthe |
| Publisher | Norwegian University of Science and Technology, Department of Engineering Cybernetics, Institutt for teknisk kybernetikk |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, text |
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