Abstract
Surface electromyography (sEMG) has recently been commonly used in sign language recognition owing to its numerous advantages (i.e., portability, lightweight, and ease of use) over cameras and inertial sensors. We proposed a real-time Chinese sign language recognition model based on sEMG signals in this research. sEMG data was collected using MYO armband on nine healthy volunteers who performed a series of 15 gestures. The signal was preprocessed using a sliding window method followed by a muscle detection operation. Five time-domain features were extracted from the preprocessed signal for feature extraction. We used a feed-forward Artificial Neural Network (ANN) to classify the signals with an activation time threshold to recognize the gestures. This model is 94.9% in accuracy and reacts in around 195.19 ms (real-time).
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Riaz, M.M., Zhang, Z. (2021). Surface EMG Real-Time Chinese Language Recognition Using Artificial Neural Networks. In: Fei, M., Chen, L., Ma, S., Li, X. (eds) Intelligent Life System Modelling, Image Processing and Analysis. LSMS ICSEE 2021 2021. Communications in Computer and Information Science, vol 1467. Springer, Singapore. https://doi.org/10.1007/978-981-16-7207-1_12
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DOI: https://doi.org/10.1007/978-981-16-7207-1_12
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