In recent years, studies, products, and services that used artificial intelligence and physiological sensor s have been published. One of the trends is classifying the user's context, such as their emotional state, using physiological data. One of the motivations for classifying emotion classification studies is that emotions influence human behaviors, decision making, health, learning efficiency. Many emotion classifications studies have been conducted; however, none of the studies classified boredom using electroencephalogram (EEG) and galvanic skin response (GSR) data. Furthermore, studies that targeted Alzheimer's disease (AD) patients with dementia for classifying their emotions did not exist. In this study, we did a literature review related to emotion classification based on physiological sensors and set healthy people (13 males and 15 females, mean age 23.62) and AD patients with dementia (30 females, mean age 83.9) as target groups. Then, we designed a data collection protocol for each target group and collected physiological data (healthy group: EEG and GSR, and AD patients with dementia group: EEG data) when exposed to video and image stimuli designed to evoke the target emotions. Using the data, I trained emotion classification models with conventional machine learning and deep learning algorithms. As a result, the model trained with multilayer perceptron (MLP) showed 79.98% mean accuracy from 1,000 iterations of five-fold cross-validation. For the model of AD patients with dementia, the ensemble model that consisted of MLP and convolution neural network showed 73.33% accuracy from leave-one-out cross-validation. Additionally, we analyzed the correlations between boredom state and collected data and indicated that healthy people's approach to classifying emotion is possible for AD patients with dementia. These results can be utilizing for affective computing systems and understanding correlations between emotional states and physiological responses.
