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Open AccessReview

Assessing Eating Behaviour Using Upper Limb Mounted Motion Sensors: A Systematic Review

1
School of Electrical Engineering and Computing, Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW 2308, Australia
2
Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle, Callaghan, NSW 2308, Australia
3
School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Callaghan, NSW 2308, Australia
*
Author to whom correspondence should be addressed.
Nutrients 2019, 11(5), 1168; https://doi.org/10.3390/nu11051168
Received: 28 February 2019 / Revised: 21 May 2019 / Accepted: 22 May 2019 / Published: 24 May 2019
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Abstract

Wearable motion tracking sensors are now widely used to monitor physical activity, and have recently gained more attention in dietary monitoring research. The aim of this review is to synthesise research to date that utilises upper limb motion tracking sensors, either individually or in combination with other technologies (e.g., cameras, microphones), to objectively assess eating behaviour. Eleven electronic databases were searched in January 2019, and 653 distinct records were obtained. Including 10 studies found in backward and forward searches, a total of 69 studies met the inclusion criteria, with 28 published since 2017. Fifty studies were conducted exclusively in laboratory settings, 13 exclusively in free-living settings, and three in both settings. The most commonly used motion sensor was an accelerometer (64) worn on the wrist (60) or lower arm (5), while in most studies (45), accelerometers were used in combination with gyroscopes. Twenty-six studies used commercial-grade smartwatches or fitness bands, 11 used professional grade devices, and 32 used standalone sensor chipsets. The most used machine learning approaches were Support Vector Machine (SVM, n = 21), Random Forest (n = 19), Decision Tree (n = 16), Hidden Markov Model (HMM, n = 10) algorithms, and from 2017 Deep Learning (n = 5). While comparisons of the detection models are not valid due to the use of different datasets, the models that consider the sequential context of data across time, such as HMM and Deep Learning, show promising results for eating activity detection. We discuss opportunities for future research and emerging applications in the context of dietary assessment and monitoring. View Full-Text
Keywords: eating activity detection; hand-to-mouth movement; wrist-mounted motion tracking sensor; accelerometer; gyroscope eating activity detection; hand-to-mouth movement; wrist-mounted motion tracking sensor; accelerometer; gyroscope
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Heydarian, H.; Adam, M.; Burrows, T.; Collins, C.; Rollo, M.E. Assessing Eating Behaviour Using Upper Limb Mounted Motion Sensors: A Systematic Review. Nutrients 2019, 11, 1168.

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