Recognition of dietary activity events using on-body sensors
Artificial Intelligence in Medicine
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analysis and detection of food patterns is an important component of an automatic dietary monitoring system. However chewing is a time-variable process depending on food properties. We present an automated methodology to extract sub-sequences of similar chews from chewing sound recordings. The approach is based on a chew-accurate segmentation of the sound signal, a multi-objective evolutionary search for temporal partitions in the sequence using NSGA-II and a validation of the best solution by classification. We evaluate the method on chewing sound recordings from a four participant study, eating foods with different rheological properties. The proposed methodology allows to determine the most appropriate partitioning of the sequences and extract relevant sound features at the same time. Potato chips and chocolate showed a two-phase structure, for lasagne and apples a single-phase structure was derived. The results led to the hypothesis that a sequential structure can be found in chewing sounds from brittle or rigid foods.