A deep learning approach to on-node sensor data analytics for mobile or wearable devices
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Published version
Author(s)
Ravi, D
Wong, C
Lo, B
Yang, G
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.
Date Issued
2017-01-01
Date Acceptance
2016-11-18
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
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/N023242/1
EP/L014149/1
EP/H009744/1
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Information Systems
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Medical Informatics
Computer Science
ActiveMiles
deep learning
Human Activity Recognition (HAR)
Internet-of-Things (IoT)
low-power devices
wearable
ACTIVITY RECOGNITION
CLASSIFIERS
Human Activities
Humans
Machine Learning
Monitoring, Ambulatory
Neural Networks, Computer
Signal Processing, Computer-Assisted
Humans
Monitoring, Ambulatory
Human Activities
Signal Processing, Computer-Assisted
Machine Learning
Neural Networks, Computer
Publication Status
Published
Date Publish Online
2016-12-23