Deep learning for human activity recognition: A resource efficient implementation on low-power devices

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Title: Deep learning for human activity recognition: A resource efficient implementation on low-power devices
Authors: Ravi, D
Wong, C
Lo, B
Yang, GZ
Item Type: Conference Paper
Abstract: Human Activity Recognition provides valuable contextual information for wellbeing, healthcare, and sport applications. Over the past decades, many machine learning approaches have been proposed to identify activities from inertial sensor data for specific applications. Most methods, however, are designed for offline processing rather than processing on the sensor node. In this paper, a human activity recognition technique based on a deep learning methodology is designed to enable accurate and real-time classification for low-power wearable devices. To obtain invariance against changes in sensor orientation, sensor placement, and in sensor acquisition rates, we design a feature generation process that is applied to the spectral domain of the inertial data. Specifically, the proposed method uses sums of temporal convolutions of the transformed input. Accuracy of the proposed approach is evaluated against the current state-of-the-art methods using both laboratory and real world activity datasets. A systematic analysis of the feature generation parameters and a comparison of activity recognition computation times on mobile devices and sensor nodes are also presented.
Issue Date: 21-Jul-2016
Date of Acceptance: 26-May-2016
URI: http://hdl.handle.net/10044/1/34176
DOI: http://dx.doi.org/10.1109/BSN.2016.7516235
ISBN: 978-1-5090-3087-3
ISSN: 2376-8894
Publisher: IEEE
Start Page: 71
End Page: 76
Journal / Book Title: Proceedings of the 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks
Copyright Statement: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Commission of the European Communities
Funder's Grant Number: EP/L014149/1
618080
Conference Name: 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks
Publication Status: Published
Start Date: 2016-06-14
Finish Date: 2016-06-17
Conference Place: San Francisco, CA, USA
Appears in Collections:Faculty of Engineering
Division of Surgery
Computing
Faculty of Medicine



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