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From smart to deep: Robust activity recognition on smartwatches using deep learning

Bhattacharya, S; Lane, ND; (2016) From smart to deep: Robust activity recognition on smartwatches using deep learning. In: Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). IEEE: Sydney, Australia. Green open access

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Abstract

The use of deep learning for the activity recognition performed by wearables, such as smartwatches, is an understudied problem. To advance current understanding in this area, we perform a smartwatch-centric investigation of activity recognition under one of the most popular deep learning methods - Restricted Boltzmann Machines (RBM). This study includes a variety of typical behavior and context recognition tasks related to smartwatches (such as transportation mode, physical activities and indoor/outdoor detection) to which RBMs have previously never been applied. Our findings indicate that even a relatively simple RBM-based activity recognition pipeline is able to outperform a wide-range of common modeling alternatives for all tested activity classes. However, usage of deep models is also often accompanied by resource consumption that is unacceptably high for constrained devices like watches. Therefore, we complement this result with a study of the overhead of specifically RBM-based activity models on representative smartwatch hardware (the Snapdragon 400 SoC, present in many commercial smartwatches). These results show, contrary to expectation, RBM models for activity recognition have acceptable levels of resource use for smartwatch-class hardware already on the market. Collectively, these two experimental results make a strong case for more widespread adoption of deep learning techniques within smartwatch designs moving forward.

Type: Proceedings paper
Title: From smart to deep: Robust activity recognition on smartwatches using deep learning
Event: 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)
ISBN-13: 9781509019410
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/PERCOMW.2016.7457169
Publisher version: http://dx.doi.org/10.1109/PERCOMW.2016.7457169
Language: English
Additional information: Copyright © 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.
Keywords: Computational modeling, Machine learning, Mathematical model, Mobile communication, Pipelines, Sensors, Transportation
UCL classification: UCL > Provost and Vice Provost Offices
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: http://discovery.ucl.ac.uk/id/eprint/1503672
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