3 papers
eess.SP2020
A temporal-to-spatial deep convolutional neural network for classification of hand movements from multichannel electromyography data
Adam Hartwell, Visakan Kadirkamanathan, Sean R. Anderson
Deep convolutional neural networks (CNNs) are appealing for the purpose of classification of hand movements from surface electromyography (sEMG) data because they have the ability…
cs.CV2019
Foveated image processing for faster object detection and recognition in embedded systems using deep convolutional neural networks
Uziel Jaramillo-Avila, Sean R. Anderson
Object detection and recognition algorithms using deep convolutional neural networks (CNNs) tend to be computationally intensive to implement. This presents a particular challenge…
cs.CV2018
Compact Deep Neural Networks for Computationally Efficient Gesture Classification From Electromyography Signals
Adam Hartwell, Visakan Kadirkamanathan, Sean R Anderson
Machine learning classifiers using surface electromyography are important for human-machine interfacing and device control. Conventional classifiers such as support vector machines…