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Your search keyword '"Athanassios N. Skodras"' showing total 151 results
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151 results on '"Athanassios N. Skodras"'

27. The Effect of Space-filling Curves on the Efficiency of Hand Gesture Recognition Based on sEMG Signals

Author

Bruno Cornelis, Jan Cornelis, Panagiotis Tsinganos, Bart Jansen, Athanassios N. Skodras, Multidimensional signal processing and communication, Electronics and Informatics, Faculty of Engineering, Vriendenkring VUB, and Audio Visual Signal Processing

Subjects
electromyography, 030506 rehabilitation, Computer Networks and Communications, Computer science, Z-order curve, 02 engineering and technology, space-filling curve, Space-filling curve, Convolutional neural network, Field (computer science), 03 medical and health sciences, Peano curve, sEMG, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Contextual image classification, business.industry, Deep learning, Pattern recognition, Hilbert curve, hand gesture recognition, classification, CNN, Deep Learning, Hardware and Architecture, Gesture recognition, 020201 artificial intelligence & image processing, Artificial intelligence, 0305 other medical science, business
Abstract

Over the past few years, Deep learning (DL) has revolutionized the field of data analysis. Not only are the algorithmic paradigms changed, but also the performance in various classification and prediction tasks has been significantly improved with respect to the state-of-the-art, especially in the area of computer vision. The progress made in computer vision has produced a spillover in many other domains, such as biomedical engineering. Some recent works are directed towards surface electromyography (sEMG) based hand gesture recognition, often addressed as an image classification problem and solved using tools such as Convolutional Neural Networks (CNN). This paper extends our previous work on the application of the Hilbert space-filling curve for the generation of image representations from multi-electrode sEMG signals, by investigating how the Hilbert curve compares to the Peano- and Z-order space-filling curves. The proposed space-filling mapping methods are evaluated on a variety of network architectures and in some cases yield a classification improvement of at least 3%, when used to structure the inputs before feeding them into the original network architectures.

Published
2021

40. Hilbert sEMG data scanning for hand gesture recognition based on deep learning

Author

Bart Jansen, Athanassios N. Skodras, Bruno Cornelis, Jan Cornelis, Panagiotis Tsinganos, Electronics and Informatics, Faculty of Engineering, Vriendenkring VUB, Translational Imaging Research Alliance, and Audio Visual Signal Processing

Subjects
Contextual image classification, Computer science, business.industry, Deep learning, 0206 medical engineering, Pattern recognition, Image processing, Hilbert curve, 02 engineering and technology, 020601 biomedical engineering, Convolutional neural network, Field (computer science), Artificial Intelligence, Gesture recognition, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, Software
Abstract

Deep learning has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed toward surface electromyography (sEMG)-based gesture recognition, often addressed as an image classification problem using convolutional neural networks (CNNs). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals, which allows the application of typical image processing pipelines such as CNNs on sequence data. The proposed method is evaluated on different state-of-the-art network architectures and yields a significant classification improvement over the approach without the Hilbert curve. Additionally, we develop a new network architecture (MSHilbNet) that takes advantage of multiple scales of an initial Hilbert curve representation and achieves equal performance with fewer convolutional layers.

Published
2020

50. Transfer Learning in sEMG-based Gesture Recognition

Author

Athanassios N. Skodras, Bruno Cornelis, Jan Cornelis, Panagiotis Tsinganos, Bart Jansen, Multidimensional signal processing and communication, Electronics and Informatics, Faculty of Engineering, Vriendenkring VUB, and Audio Visual Signal Processing

Subjects
Scheme (programming language), electromyography, Computer science, business.industry, gesture recognition, Deep learning, SIGNAL (programming language), Pattern recognition, Convolutional neural network, transfer learning, Field (computer science), Gesture recognition, Preprocessor, Artificial intelligence, business, Transfer of learning, computer, computer.programming_language
Abstract

The latest advancements in the field of deep learning and biomedical engineering have allowed for the development of myoelectric interfaces based on deep neural networks. A longstanding problem of these interfaces is that the models cannot easily be applied to new users due to the high variability and stochastic nature of the electromyography signals. Further training a new model for every new subject requires the collection of large volumes of data. Therefore, this work proposes a transfer learning (TL) scheme which allows reusing the knowledge of a pre-existing model for a new user. Firstly, a convolutional neural network (CNN) is trained on an initial dataset using the data of multiple subjects. Then, the weights of this model are fine-tuned for a new target subject. The approach is evaluated on the Ninapro datasets DB2 and DB7. The experimentation included three different CNN models and eight preprocessing alternatives. The results showed that the success of the TL method depends on how the data are preprocessed. Specifically, the biggest accuracy improvement (+5.14%) is achieved when only the first 20% of the signal duration is used.

Published
2021

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