Detection Of Forearm Movements Using Wavelets And Adaptive Neuro-Fuzzy Inference System (ANFIS)

Guvenc S. A., Demir M., ULUTAŞ M.

IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA), Alberobello, Italy, 23 - 25 June 2014, pp.192-196 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/inista.2014.6873617
  • City: Alberobello
  • Country: Italy
  • Page Numbers: pp.192-196
  • Karadeniz Technical University Affiliated: Yes


In this paper, a technique to classify seven different forearm movements using surface electromyography (sEMG) data which were received from 8 able bodied subjects was proposed. A 2-channel sEMG system was used for data acquisition and recording, then this raw electromyography (EMG) signals were applied to the wavelet denoising. In the next step, time-frequency feature is extracted calculating wavelet packet transform (WPT) coefficients for the offline classification. Feature vector of EMG signals were formed using only node energy of the WPT coefficients. In conclusion, seven forearm movements were separated by Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier and 92% success ratios over 500 samples were obtained.