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.
Highlights
Recent advances in the accuracy and accessibility of wearable sensing technology has allowed researchers and practitioners to utilise motion sensors mounted on the upper limbs to assess dietary intake and eating behaviour in both laboratory and free-living conditions
One can use the data gained from upper limb motion sensors to (1) improve and complement traditional dietary assessment methods [11]
= Hidden Markov Model, HTM = Hierarchical Temporal Memory, Jour = Journal, KM = K-Means, 653 studies were screened on title and abstract
Summary
Recent advances in the accuracy and accessibility of wearable sensing technology (e.g., commercial inertial sensors, fitness bands, and smart watches) has allowed researchers and practitioners to utilise motion sensors mounted on the upper limbs (i.e., lower arm/wrist, upper arm) to assess dietary intake and eating behaviour in both laboratory and free-living conditions Inertial sensors such as accelerometers (e.g., [1,2]) and gyroscopes (e.g., [3,4]), as well as proximity sensors (e.g., radio-frequency identification (RFID) [5,6]), can be used to detect and quantify characteristic hand-to-mouth gestures associated with food and beverage consumption. Sensors have substantially reduced in size, operate wirelessly, and are powered by rechargeable batteries (e.g., [9,14])
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