Locomo-Net: A Low -Complex Deep Learning Framework for sEMG-Based Hand Movement Recognition for Prosthetic Control

Background: The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named 'Low-Complex Movement rec...

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Veröffentlicht in:	IEEE journal of translational engineering in health and medicine 2020-01, Vol.8, p.1-12
Hauptverfasser:	Gautam, Arvind, Panwar, Madhuri, Wankhede, Archana, Arjunan, Sridhar P., Naik, Ganesh R., Acharyya, Amit, Kumar, Dinesh K.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks CNN Complexity Computing time Convolution Data compression Datasets Deep learning Electrodes Electromyography Feature extraction Field programmable gate arrays Lookup tables Machine learning Model accuracy Motion perception movement classification Muscles Myoelectricity Pattern recognition Power consumption Prostheses Real-time systems sEMG signal processing Support vector machines Task analysis weights compression Wrist
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