چکیده مقاله
The current paper presents Particle Swarm Optimized Wavelet Neural Network PSOWNN as a classification method for surface electromyogram sEMG pattern classification According to the literature, a change in the spectrum of surface electromyogram has largely been attributed to the change in muscle conduction velocity Therefore, such signals are used to command a robot using a WNN classifier During the experiments, the subjects are instructed by an auditory cue to elicit a contraction from the rest state and hold that finger posture for a period of 5 seconds For this purpose, two EMG electrodes attached to the human forearm are utilized to collect the EMG data Time and frequency characteristics such as Number of Zero Crossings ZC , Autoregressive AR , and wavelet coefficients are considered as features And, WNN as a classification method is optimized using particle swarm optimization algorithm The accuracy of PSOWNN is compared to that of Artificial Neural Network ANN The results show an accuracy of 90% for the proposed method, indicating a better performance than ANN in terms of accuracy Finally, outputs of the best classification method are implemented on a robot
کلیدواژهها
نویسندگان
Maryam Alimohammadi Soltanmoradi
Department of Mechatronics, School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran
Vahid Azimirad
Department of Mechatronics, School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran
Farajollah Tahernezhad-Javazm
Department of Mechatronics, School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran
شیوه ارجاع
Alimohammadi Soltanmoradi, Maryam and Azimirad, Vahid and Tahernezhad-Javazm, Farajollah,1396,Classification of EMG Signals through Wavelet Neural Network for Finger-Robot Interface,Fifth International Conference on Electrical and Computer Engineering with Emphasis on Indigenous Knowledge,Tehran
ارائهشده در
مجموعه مقالات پنجمین کنفرانس بین المللی مهندسی برق و کامپیوتر با تاکید بر دانش بومی19 بهمن 1396 · تهران