Search Results for author:
Found 3 papers, 0 papers with code
A temporal-to-spatial deep convolutional neural network for classification of hand movements from multichannel electromyography data
The temporal layer captures these signature features for each channel separately, which are then spatially mixed in successive layers to recognise a specific gesture.
Foveated image processing for faster object detection and recognition in embedded systems using deep convolutional neural networks
Object detection and recognition algorithms using deep convolutional neural networks (CNNs) tend to be computationally intensive to implement.
Compact Deep Neural Networks for Computationally Efficient Gesture Classification From Electromyography Signals
The accuracy of the compact deep net was found to be 84. 2 +/- 6% versus 70. 5 +/- 7% for the SVM on the Myo, and 80. 3+/- 7% versus 67. 8 +/- 9% for the Delsys system, demonstrating the superior effectiveness of the proposed compact network, which had just 5, 889 parameters - orders of magnitude less than some contemporary alternatives in this domain while maintaining better performance.
