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

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.

A review on intelligent wearables: Uses and risks

“…Diet control is another way of weight management. Previous studies compared and confirmed the effectiveness of wearable devices in monitoring people's dietary behaviors (Heydarian, Adam, Burrows, Collins, & Rollo, 2019;Schiboni & Amft, 2018;Vu et al, 2017).…”

Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review

“…Detecting and monitoring human eating behavior in the realworld is a growing field and offers exciting and novel opportunities for future public health research. Previous literature reviews on automatic eating detection methods have often placed a focus on sensor modalities and technologies in any research setting 28 , on specific sensor modalities, such as wearable video cameras 65 , or on specific sensor locations, such as the upper limb 29 . No review to date has focused specifically on studies that use wearable devices to automatically detect eating activity in naturalistic settings.…”

“…In the past decade, a range of wearable sensors for the purpose of automating eating detection have been proposed and studied. However, these studies have been primarily conducted in a combination of controlled lab and semi-controlled field settings 28,29 , and for good reason: these systems are challenging to develop, deploy, and evaluate. More recently, however, the research field has rapidly expanded the testing of these devices in the field.…”

Future directions for integrative objective assessment of eating using wearable sensing technology

“…A key aspect of smartwatches and activity monitors is that individuals wear and use them as part of their daily routine. Recent reviews concluded that smartwatches in particular represent an unobtrusive solution for both the tracking of eating behaviour 58 , and the delivery of targeted, context-sensitive recommendations promoting positive health outcomes 59 , such as Just-in-Time interventions 60 .…”