The paper tries to recognize EMG signals by using neural networks. The electrodes under the dry state are attached to wrists and then EMG is measured. These EMG signals are classified into seven categories, such as neutral, up and down, right and left, wrist to inside, wrist to outside by using a neural network. The neural network learns FFT spectra to classify them. Moreover, we perform the principal component analysis using the simple principal component analysis before we perform recognition experiments. It is shown that our approach is effective to classify the EMG signals by means of computer simulations.
|Title of host publication
|ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2002
|9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore
Duration: 2002 Nov 18 → 2002 Nov 22
|9th International Conference on Neural Information Processing, ICONIP 2002
|02/11/18 → 02/11/22
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing