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This paper presents the acquisition system that is able to classify hand gestures using electromyography (EMG) signals from a MYO armband and/or the finger positions using a 5DT data glove. Both devices make the EMG recording system easier because the hand gesture is identified automatically by the data captured from the 5DT data glove. The recorded EMG data can be analyzed using classification techniques, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Artificial neural network (ANN), for creating the hand gesture prediction model. The proposed system can classify three gestures, including rest hand, close hand, and open hand. The recorded data using the MYO armband, the 5DT data glove, and the ANN technique provide maximum accuracy of 95.95% with ±1.13 SD while the captured data using the MYO solely with ANN deliver maximum accuracy of 97.37% with ±2.83 SD. Due to overlap variance, both performances are not significantly different. The distance-based method is also used for analyzing the system. The time consumption to record the relevant information using the MYO armband and the 5DT data glove is approximately 5 minutes. However, the recorded system using the MYO armband solely takes around 12 minutes due to manual data registration process. Subsequently, the time consumptions of these two systems show that the data recording system based on both devices is 2.4 times faster than that of using the MYO armband only.
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