R-CNN and Wavelet Feature Extraction for Hand Gesture Recognition With Emg Signals
Document Type
Article
Publication Date
11-1-2020
Description
This paper demonstrates the implementation of R-CNN in terms of electromyography-related signals to recognize hand gestures. The signal acquisition is implemented using electrodes situated on the forearm, and the biomedical signals are generated to perform the signals preprocessing using wavelet packet transform to perform the feature extraction. The R-CNN methodology is used to map the specific features that are acquired from the wavelet power spectrum to validate and train how the architecture is framed. Additionally, the real-time test is completed to reach the accuracy of 96.48% compared to the related methods. This kind of result proves that the proposed work has the highest amount of accuracy in recognizing the gestures.
Citation Information
Shanmuganathan, Vimal; Yesudhas, Harold Robinson; Khan, Mohammad S.; Khari, Manju; and Gandomi, Amir H.. 2020. R-CNN and Wavelet Feature Extraction for Hand Gesture Recognition With Emg Signals. Neural Computing and Applications. Vol.32(21). 16723-16736. https://doi.org/10.1007/s00521-020-05349-w ISSN: 0941-0643