Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 14; p. 3170
• Main Authors: Zhang, Zhen, Yang, Kuo, Qian, Jinwu, Zhang, Lunwei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.07.2019; MDPI
• Subjects: Accuracy; Adult; Algorithms; Artificial intelligence; artificial neural network; Biosensing Techniques; Classification; Electromyography; Engineering; Female; gesture recognition; Gestures; Hand - physiology; Humans; International conferences; Machine learning; Male; Neural networks; Neural Networks, Computer; Pattern recognition; Pattern Recognition, Automated; real-time; Researchers; Sensors; Signal processing; Signal Processing, Computer-Assisted; surface electromyography; United States > US; China; artificial neural network; gesture recognition; real-time; surface electromyography
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19143170

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.