LabelSens: enabling real-time sensor data labelling at the point of collection using an artificial intelligence-based approach

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Woodward, K ORCID: https://orcid.org/0000-0003-3302-1345, Kanjo, E ORCID: https://orcid.org/0000-0002-1720-0661, Oikonomou, A ORCID: https://orcid.org/0000-0002-5069-3971 and Chamberlain, A, 2020. LabelSens: enabling real-time sensor data labelling at the point of collection using an artificial intelligence-based approach. Personal and Ubiquitous Computing. ISSN 1617-4909

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Official URL: https://doi.org/10.1007/s00779-020-01427-x

Abstract

In recent years, machine learning has developed rapidly, enabling the development of applications with high levels of recognition accuracy relating to the use of speech and images. However, other types of data to which these models can be applied have not yet been explored as thoroughly. Labelling is an indispensable stage of data pre-processing that can be particularly challenging, especially when applied to single or multi-model real-time sensor data collection approaches. Currently, real-time sensor data labelling is an unwieldy process, with a limited range of tools available and poor performance characteristics, which can lead to the performance of the machine learning models being compromised. In this paper, we introduce new techniques for labelling at the point of collection coupled with a pilot study and a systematic performance comparison of two popular types of deep neural networks running on five custom built devices and a comparative mobile app (68.5-89% accuracy within-device GRU model, 92.8% highest LSTM model accuracy). These devices are designed to enable real-time labelling with various buttons, slide potentiometer and force sensors. This exploratory work illustrates several key features that inform the design of data collection tools that can help researchers select and apply appropriate labelling techniques to their work. We also identify common bottlenecks in each architecture and provide field tested guidelines to assist in building adaptive, high-performance edge solutions.

Item Type:	Journal article
Publication Title:	Personal and Ubiquitous Computing
Creators:	Woodward, K., Kanjo, E., Oikonomou, A. and Chamberlain, A.
Publisher:	Springer Science and Business Media LLC
Date:	27 June 2020
ISSN:	1617-4909
Identifiers:	Number Type 10.1007/s00779-020-01427-x DOI 1339791 Other
Rights:	© The Author(s) 2020. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > School of Science and Technology
Record created by:	Jill Tomkinson
Date Added:	13 Jul 2020 12:50
Last Modified:	31 May 2021 15:18
URI:	https://irep.ntu.ac.uk/id/eprint/40213