The invention discloses a probabilistic
context free grammar for
food intake. The invention regards monitoring and modification of
eating behavior through continuous
meal weight measurements, and hasbeen successfully applied in clinical practice to treat
obesity and
eating disorders. For this purpose, the Mandometer R
trademark, a plate scale, along with video recordings of subjects during the course of single meals, has been used to assist clinicians in measuring relevant
food intake parameters. In this work, we present a novel
algorithm for automatically constructing a subject's food intakecurve using only the Mandometer R
trademark weight measurements. This eliminates the need for direct clinical observation or video recordings, thus significantly reducing the manual effort required for analysis. The proposed
algorithm aims at identifying specific
meal related events (e.g. bites, food additions, artifacts), by applying an adaptive pre-
processing stage using
Delta coefficients, followed by event detection based on a parametric Probabilistic Context-Free Grammar on the derivative of the recorded sequence. Experimental results on a dataset of 114 meals from individuals sufferingfrom
obesity or
eating disorders, as well as from individuals with normal BMI, demonstrate the effectiveness of the proposed approach.