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A deep learning approach to on-node sensor data analytics for mobile or wearable devices

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Title: A deep learning approach to on-node sensor data analytics for mobile or wearable devices
Authors: Ravi, D
Wong, C
Lo, B
Yang, G
Item Type: Journal Article
Abstract: The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. Whilst deep learning has been successful in implementations that utilize high performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learnt from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning framework where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain pre-processing is used before the data is passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform.
Issue Date: 1-Jan-2017
Date of Acceptance: 18-Nov-2016
URI: http://hdl.handle.net/10044/1/42700
DOI: 10.1109/JBHI.2016.2633287
ISSN: 2168-2208
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Start Page: 56
End Page: 64
Journal / Book Title: IEEE Journal of Biomedical and Health Informatics
Volume: 21
Issue: 1
Copyright Statement: © 2016 The Authors. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
Sponsor/Funder: Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/N023242/1
Keywords: Science & Technology
Life Sciences & Biomedicine
Computer Science, Information Systems
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Medical Informatics
Computer Science
deep learning
Human Activity Recognition (HAR)
Internet-of-Things (IoT)
low-power devices
Human Activities
Machine Learning
Monitoring, Ambulatory
Neural Networks, Computer
Signal Processing, Computer-Assisted
Monitoring, Ambulatory
Human Activities
Signal Processing, Computer-Assisted
Machine Learning
Neural Networks, Computer
Publication Status: Published
Online Publication Date: 2016-12-23
Appears in Collections:Department of Surgery and Cancer
Faculty of Medicine
Institute of Global Health Innovation
Faculty of Engineering