Ravi, D;
Wong, C;
Lo, B;
Yang, G-Z;
(2017)
A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices.
IEEE Journal of Biomedical and Health Informatics
, 21
(1)
pp. 56-64.
10.1109/JBHI.2016.2633287.
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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. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on lowpower wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned 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 preprocessing is used before the data are 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 onnode processing on smartphones and a wearable sensor platform.
| Type: | Article |
|---|---|
| Title: | A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/JBHI.2016.2633287 |
| Publisher version: | http://doi.org/10.1109/JBHI.2016.2633287 |
| Language: | English |
| Additional information: | Copyright © Authors 2017. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ |
| Keywords: | ActiveMiles, deep learning, Human Activity Recognition (HAR), Internet-of-Things (IoT), low-power devices, wearable. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10041080 |
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