  The traditional dietary recommendation systems are basically nutrition or
health-aware where the human feelings on food are ignored. Human affects vary
when it comes to food cravings, and not all foods are appealing in all moods. A
questionnaire-based and preference-aware meal recommendation system can be a
solution. However, automated recognition of social affects on different foods
and planning the menu considering nutritional demand and social-affect has some
significant benefits of the questionnaire-based and preference-aware meal
recommendations. A patient with severe illness, a person in a coma, or patients
with locked-in syndrome and amyotrophic lateral sclerosis (ALS) cannot express
their meal preferences. Therefore, the proposed framework includes a
social-affective computing module to recognize the affects of different meals
where the person's affect is detected using electroencephalography signals. EEG
allows to capture the brain signals and analyze them to anticipate affective
toward a food. In this study, we have used a 14-channel wireless Emotive Epoc+
to measure affectivity for different food items. A hierarchical ensemble method
is applied to predict affectivity upon multiple feature extraction methods and
TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is
used to generate a food list based on the predicted affectivity. In addition to
the meal recommendation, an automated menu planning approach is also proposed
considering a person's energy intake requirement, affectivity, and nutritional
values of the different menus. The bin-packing algorithm is used for the
personalized menu planning of breakfast, lunch, dinner, and snacks. The
experimental findings reveal that the suggested affective computing, meal
recommendation, and menu planning algorithms perform well across a variety of
assessment parameters.
