@inproceedings{scholars8703,
         journal = {2016 2nd International Conference of Industrial, Mechanical, Electrical, and Chemical Engineering, ICIMECE 2016},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
           title = {Electromyography (EMG) signal recognition using combined discrete wavelet transform based on Artificial Neural Network (ANN)},
           pages = {95--99},
            note = {cited By 9; Conference of 2nd International Conference of Industrial, Mechanical, Electrical, and Chemical Engineering, ICIMECE 2016 ; Conference Date: 6 October 2016 Through 7 October 2016; Conference Code:127471},
            year = {2017},
             doi = {10.1109/ICIMECE.2016.7910421},
        keywords = {Discrete wavelet transforms; Electromyography; Neural networks; Pattern recognition; Pattern recognition systems; Prosthetics; Wavelet transforms, Accuracy rate; Advanced researches; Controller systems; Program control; Prosthetic hands; Research focus; Science development; Signal recognition, Biomedical signal processing},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019438819&doi=10.1109\%2fICIMECE.2016.7910421&partnerID=40&md5=66fcea000d4898192f558eb64e26ece4},
        abstract = {Rapid disability patients increasing over time and need a solution in the future. Hand amputation is one form of disability that common in Indonesian society. A possible solution would be necessary at the moment is the development of prosthetic hand that has the ability as a human hand. The development of neuroscience has now reached the stage of the body's ability to use the signal as an input signal to operate a system. One of the applications of the science development is the use of electromyography (EMG) signals as an input to the control system to operate the prosthetic hand. This study is divided into two stages: a preliminary study and further research. Initial research focus in the process of EMG signal pattern recognition and advanced research focus in the development of a prototype prosthetic hand that is integrated with the controller system. Preliminary research indicates that the results of pattern recognition EMG signal using wavelet transform and Artificial Neural Network (ANN) classification has an accuracy rate of about 77.5 . Based on these results, it can be concluded that the study results could be used as a signal input to program control of the prosthetic hand that will be developed in phase two. {\^A}{\copyright} 2016 IEEE.},
          author = {Arozi, M. and Putri, F. T. and Ariyanto, M. and Caesarendra, W. and Widyotriatmo, A. and Munadi, {} and Setiawan, J. D.},
            isbn = {9781467385046}
}