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.

翻译：传统膳食推荐系统主要基于营养或健康因素，忽略了人对食物的主观感受。人类对食物的渴望存在情感差异，且并非所有食物在任何情绪状态下都具有吸引力。基于问卷调查和偏好感知的膳食推荐系统可作为一种解决方案。然而，自动识别不同食物的社会情感，并综合考虑营养需求与社会情感进行菜单规划，相比问卷式偏好感知推荐具有显著优势。重症患者、昏迷者、闭锁综合征患者及肌萎缩侧索硬化症患者无法表达其膳食偏好。因此，本文提出的框架包含社会情感计算模块，采用脑电图信号识别个体对不同餐食的情感状态。EEG可捕获脑电信号并分析以预测对食物的情感倾向。本研究采用14通道无线Emotive Epoc+设备测量个体对不同食物的情感响应。通过分层集成方法基于多种特征提取方法预测情感值，并采用TOPSIS（逼近理想解排序法）根据预测情感值生成食物列表。除膳食推荐外，还提出了一种自动化菜单规划方法，综合考虑个体的能量摄入需求、情感状态及不同菜单的营养价值。采用装箱算法实现早餐、午餐、晚餐及加餐的个性化菜单规划。实验结果表明，所提出的情感计算、膳食推荐及菜单规划算法在多项评估指标上均表现优异。