Abstract
In recent years, surface electromyography (sEMG) signals have been increasingly used in pattern recognition and rehabilitation. In this paper, a real-time hand gesture recognition model using sEMG is proposed. We use an armband to acquire sEMG signals and apply a sliding window approach to segment the data in extracting features. A feedforward artificial neural network (ANN) is founded and trained by the training dataset. A test method is used in which the gesture will be recognized when recognized label times reach the threshold of activation times by the ANN classifier. In the experiment, we collected real sEMG data from twelve subjects and used a set of five gestures from each subject to evaluate our model, with an average recognition rate of 98.7% and an average response time of 227.76 ms, which is only one-third of the gesture time. Therefore, the pattern recognition system might be able to recognize a gesture before the gesture is completed.
Highlights
Hand gestures are one type of communication
Based on the types of sensors mentioned above, the surface electromyography (sEMG) sensors can be applied for hand gesture recognition because they are not affected by the variations of light, position, and orientation of the hand
We propose a new gesture recognition model based on artificial neural network (ANN) and sEMG signals to achieve real-time response
Summary
Gesture recognition provides a smart, natural, and convenient human–computer interaction (HCI) approach. It is an important part of HCI and has a wide range of applications in engineering and intelligent devices. There are many sensors used in hand gesture recognition for data acquisition, including cameras [5,6,7,8], cyber gloves [9,10], surface electromyography (sEMG) [11,12], and radio frequency [13]. Based on the types of sensors mentioned above, the sEMG sensors can be applied for hand gesture recognition because they are not affected by the variations of light, position, and orientation of the hand
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