Nov 18, 2018

Gated Recurrent Neural Networks for EMG-Based Hand Gesture Classification. A Comparative Study

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Ali Samadani

Abstract

Electromyographic activities (EMG) generated during contraction of upper limb muscles can be mapped to distinct hand gestures and movements, posing them as a promising modality for prosthetic and cybernetic applications. This paper presents a comparative analysis between different recurrent neural network (RNN) configurations for EMG-based hand gesture classification. In particular, RNNs with recurrent units of long short-term memory (LSTM) and gated recurrent unit (GRU) are evaluated. Furthermore, the effects of an attention mechanism and varying learning rates are evaluated. Results show a classifier 1) with a bidirectional recurrent layer composed of LSTM units, 2) that applies the attention mechanism, and 3) trained with step-wise learning rate outperforms all other tested RNN classifiers.

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Mentioned in this Paper

Surface Electromyography
Hand
Biological Neural Networks
Diagnostic Radiology Modality
Classification
Contraction (Finding)
Upper Extremity
Electromyography
Neural Network Simulation
Muscle of Upper Limb

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