Using recurrent neural networks to compare movement patterns in ADHD and normally developing children based on acceleration signals from the wrist and ankle

Muñoz-Organero, M., Powell, L. orcid.org/0000-0003-0230-8722, Heller, B. et al. (2 more authors) (2019) Using recurrent neural networks to compare movement patterns in ADHD and normally developing children based on acceleration signals from the wrist and ankle. Sensors, 19 (13). 2935. ISSN 1424-8220

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2019 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Keywords: ADHD; tri-axial accelerometers; deep learning; Recurrent Neural Networks (RNN); Long Short Term Memory (LSTM)
Dates:
  • Accepted: 1 July 2019
  • Published (online): 3 July 2019
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > Sheffield Centre for Health and Related Research
Depositing User: Symplectic Sheffield
Date Deposited: 09 Jul 2019 13:24
Last Modified: 09 Jul 2019 14:28
Status: Published
Publisher: MDPI AG
Refereed: Yes
Identification Number: https://doi.org/10.3390/s19132935

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