Evaluating the Transferability of Personalised Exercise Recognition Models
Exercise Recognition (ExR) is relevant in many high impact domains, from healthcare to recreational activities to sports sciences. Like Human Activity Recognition (HAR), ExR faces many challenges when deployed in the real-world. For instance, typical lab
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Lazaros Iliadis Plamen Parvanov Angelov Chrisina Jayne Elias Pimenidis Editors
Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference Proceedings of the EANN 2020
Proceedings of the International Neural Networks Society Volume 2
Series Editors Plamen Angelov, School of Computing and Communications, University of Lancaster, Lancaster, UK Robert Kozma, Optimization and Networks Department, University of Memphis, Memphis, TN, USA
The “Proceedings of the International Neural Networks Society INNS” publishes research contributions on fundamental principles and applications related to neural networks and modeling behavioral and brain processes. Topics of interest include new developments, state-of-art theories, methods and practical applications, covering all aspects of neural networks and neuromorphic technologies for (artificially; replace with anthropomorphic) intelligent (designs; replace with systems). This series covers high quality books that contribute to the full range of neural networks research, from computational neuroscience, cognitive science, behavioral and brain modeling, (add machine) learning algorithms, mathematical theories, to technological applications of systems that significantly use neural network concepts and techniques. The series publishes monographs, contributed volumes, lecture notes, edited volumes, and conference proceedings in neural networks spanning theoretical, experimental, computational, and engineering aspects. Submissions of highly innovative cutting-edge contributions are encouraged, which extend our understanding at the forefront of science going beyond mainstream approaches. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output in this rapidly developing research field.
More information about this series at http://www.springer.com/series/16268
Lazaros Iliadis Plamen Parvanov Angelov Chrisina Jayne Elias Pimenidis •
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Editors
Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference Proceedings of the EANN 2020
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Editors Lazaros Iliadis School of Engineering, Department of Civil Engineering Democritus University of Thrace Xanthi, Greece Chrisina Jayne School of Computing and Digital Technologies Teesside University Middlesbrough, UK
Plamen Parvanov Angelov Lancaster University Lancaster, UK Elias Pimenidis University of the West of England Bristol, UK
ISSN 2661-8141 ISSN 2661-815X (electronic) Proceedings of the International Neural Networks Society ISBN 978-3-030-48790-4 ISBN 978-3-030-48791-1 (eBook) https://doi.org/10.1007/978-3-030-48791-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, rep
