Abstract
Eating behavior can have an important effect on, and be correlated with, obesity and eating disorders. Eating behavior is usually estimated through self-reporting measures, despite their limitations in reliability, based on ease of collection and analysis. A better and widely used alternative is the objective analysis of eating during meals based on human annotations of in-meal behavioral events (e.g., bites). However, this methodology is time-consuming and often affected by human error, limiting its scalability and cost-effectiveness for large-scale research. To remedy the latter, a novel “Rapid Automatic Bite Detection” (RABiD) algorithm that extracts and processes skeletal features from videos was trained in a video meal dataset (59 individuals; 85 meals; three different foods) to automatically measure meal duration and bites. In these settings, RABiD achieved near perfect agreement between algorithmic and human annotations (Cohen’s kappa κ = 0.894; F1-score: 0.948). Moreover, RABiD was used to analyze an independent eating behavior experiment (18 female participants; 45 meals; three different foods) and results showed excellent correlation between algorithmic and human annotations. The analyses revealed that, despite the changes in food (hash vs. meatballs), the total meal duration remained the same, while the number of bites were significantly reduced. Finally, a descriptive meal-progress analysis revealed that different types of food affect bite frequency, although overall bite patterns remain similar (the outcomes were the same for RABiD and manual). Subjects took bites more frequently at the beginning and the end of meals but were slower in-between. On a methodological level, RABiD offers a valid, fully automatic alternative to human meal-video annotations for the experimental analysis of human eating behavior, at a fraction of the cost and the required time, without any loss of information and data fidelity.
Highlights
The analysis of eating-related behavioral characteristics during meals is well-established in the field of microstructural analysis of human eating [1]
Rapid Automatic Bite Detection” (RABiD) was initially trained on an independent training dataset (TD) of meal videos recorded from individuals consuming three different food types under identical controlled conditions
This study evaluates a new methodology for the automatic behavioral analysis of meals on the level of meal duration and bites, based on video meal data recorded in controlled environments
Summary
The analysis of eating-related behavioral characteristics during meals is well-established in the field of microstructural analysis of human eating [1]. The estimation of meal behaviors has been based on self-rated measures, due to the ease and comparatively low cost of data collection, despite the significant increase on participant burden [9] and limited reliability, mostly due to erroneous reporting and reporting biases in many of the targeted populations [10]. Improving on these techniques, various methodologies for the objective quantification of eating behavior have been deployed, ranging from laboratory studies [11] to real life data collection actions [12]. The employed technologies cover a wide range of sensory modalities [13], with current advances supporting increasingly sophisticated data collection and analysis platforms
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