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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mordaunt, P. Zalzala, A.M.S. |
| Copyright Year | 2002 |
| Description | Author affiliation: Dept. of Comput. & Electr. Eng., Heriot-Watt Univ., Edinburgh, UK (Mordaunt, P.; Zalzala, A.M.S.) |
| Abstract | This paper presents initial investigations into an evolutionary neural network suitable for gait analysis of human motion. The approach here is to develop an intelligent black box that can take the physiological signals (EMG) and interpret them to give accurate information on the position and movement of the knee (gait). Two MLP networks are presented with weight evolving algorithms employing mutation and crossover separately. Simulation results show the evolutionary algorithms exhibiting particularly better ability to generalise solutions, which is an important aspect for the reliability of EMG/gait map generation. |
| Sponsorship | IEEE IEEE Neural Networks Soc. IEE Evolutionary Programming Soc |
| Starting Page | 1922 |
| Ending Page | 1927 |
| File Size | 724684 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780372824 |
| DOI | 10.1109/CEC.2002.1004537 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2002-05-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Muscles Knee Electrodes Biological neural networks Humans Electromyography Prosthetic limbs Artificial neural networks Computer networks |
| Content Type | Text |
| Resource Type | Article |
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