Performance Evaluation of Gesture Recognition Using Myo Armband and Gyroscope Sensors
DOI:
https://doi.org/10.29194/NJES.28030461Keywords:
Surface ElectoMyoGraphy, Support Vector Machine, Linear Discriminant Analysis, K-Nearest NeighborsAbstract
The technique of recording muscle signals is crucial in determining how effectively they can be utilized for individual benefit. This study focuses on hand movements recognized by using the Myo armband and Motion Processing Unit (MPU) 6050 sensors. Linear Discriminant Analysis (LDA), K-nearest neighbors (k-NN), and Support Vector Machine (SVM) were employed for classification. sEMG signals using the Myo armband for 7 hand gestures and 2 elbow movements were recorded from 10 healthy subjects. Results showed that SVM outperforms LDA and k-NN in accuracy in both cases, the sensor is worn once on the arm and again on the forearm. regions. The window size and choice of features significantly influence system accuracy, with SVM achieving an average accuracy of 89.84%. Besides that, the fusion of Myo Armband sensor and gyroscope sensor through OR rule makes significant enhancement in recognition accuracy with which is reached to 97.0135%. In conclusion, the Myo armband, when worn on the forearm, proves practical for hand gesture recognition, with SVM offering superior recognition accuracy. Furthermore, the combination of the Myo Armband sensor and the gyroscope sensor showed higher recognition accuracy.
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