A deep learning based wearable system for food and drink intake recognition

Ortega Anderez, D ORCID: https://orcid.org/0000-0003-3571-847X, Lotfi, A ORCID: https://orcid.org/0000-0002-5139-6565 and Pourabdollah, A ORCID: https://orcid.org/0000-0001-7737-1393, 2020. A deep learning based wearable system for food and drink intake recognition. Journal of Ambient Intelligence and Humanized Computing. ISSN 1868-5137

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Abstract

Eating difficulties and the subsequent need for eating assistance are a prevalent issue within the elderly population. Besides, a poor diet is considered a confounding factor for developing chronic diseases and functional limitations. Driven by the above issues, this paper proposes a wrist-worn tri-axial accelerometer based food and drink intake recognition system. First, an adaptive segmentation technique is employed to identify potential eating and drinking gestures from the continuous accelerometer readings. A posteriori, a study upon the use of Convolutional Neural Networks for the recognition of eating and drinking gestures is carried out. This includes the employment of three time series to image encoding frameworks, namely the signal spectrogram, the Markov Transition Field and the Gramian Angular Field, as well as the development of various multi-input multi-domain networks. The recognition of the gestures is then tackled as a 3-class classification problem (‘Eat’, ‘Drink’ and ‘Null’), where the ‘Null’ class is composed of all the irrelevant gestures included in the post-segmentation gesture set. An average per-class classification accuracy of 97.10% was achieved by the proposed system. When compared to similar work, such accurate classification performance signifies a great contribution to the field of assisted living.

Item Type:	Journal article
Publication Title:	Journal of Ambient Intelligence and Humanized Computing
Creators:	Ortega Anderez, D., Lotfi, A. and Pourabdollah, A.
Publisher:	Springer Science and Business Media LLC
Date:	21 November 2020
ISSN:	1868-5137
Identifiers:	Number Type 10.1007/s12652-020-02684-7 DOI 1393015 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:	Linda Sullivan
Date Added:	07 Dec 2020 15:10
Last Modified:	31 May 2021 15:07
URI:	https://irep.ntu.ac.uk/id/eprint/41801

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