Meal Microstructure Characterization from Sensor-Based Food Intake Detection


To avoid the pitfalls of self-reported dietary intake, wearable sensors can be used. Many food ingestion sensors offer the ability to automatically detect food intake using time resolutions that range from 23 ms to 8 min. There is no defined standard time resolution to accurately measure ingestive behavior or a meal microstructure. This paper aims to estimate the time resolution needed to accurately represent the microstructure of meals such as duration of eating episode, the duration of actual ingestion, and number of eating events. Twelve participants wore the automatic ingestion monitor (AIM) and kept a standard diet diary to report their food intake in free-living conditions for 24 h. As a reference, participants were also asked to mark food intake with a push button sampled every 0.1 s. The duration of eating episodes, duration of ingestion, and number of eating events were computed from the food diary, AIM, and the push button resampled at different time resolutions (0.1-30s). ANOVA and multiple comparison tests showed that the duration of eating episodes estimated from the diary differed significantly from that estimated by the AIM and the push button (p-value <0.001). There were no significant differences in the number of eating events for push button resolutions of 0.1, 1, and 5 s, but there were significant differences in resolutions of 10-30s (p-value <0.05). The results suggest that the desired time resolution of sensor-based food intake detection should be <= 5 s to accurately detect meal microstructure. Furthermore, the AIM provides more accurate measurement of the eating episode duration than the diet diary.

food intake detection, food diary, swallowing, chewing, wearable sensors, meal microstructure, BITE SIZE, OBESE, PATTERNS, BEHAVIOR, HUMANS, DEVICE, CHEWS, EAT, Nutrition & Dietetics
Doulah, A., et al. (2017): Meal Microstructure Characterization from Sensor-Based Food Intake Detection. Frontiers in Nutrition, 4(31). DOI: 10.3389/fnut.2017.00031