In human emotion estimation using an electroencephalogram (EEG) and heart rate variability (HRV), there are two main issues as far as we know. The first is that measurement devices for physiological signals are expensive and not easy to wear. The second is that unnecessary physiological indexes have not been removed, which is likely to decrease the accuracy of machine learning models. In this study, we used single-channel EEG sensor and photoplethysmography (PPG) sensor, which are inexpensive and easy to wear. We collected data from 25 participants (18 males and 7 females) and used a deep learning algorithm to construct an emotion classification model based on Arousal–Valence space using several feature combinations obtained from physiological indexes selected based on our criteria including our proposed feature selection methods. We then performed accuracy verification, applying a stratified 10-fold cross-validation method to the constructed models. The results showed that model accuracies are as high as 90% to 99% by applying the features selection methods we proposed, which suggests that a small number of physiological indexes, even from inexpensive sensors, can be used to construct an accurate emotion classification model if an appropriate feature selection method is applied. Our research results contribute to the improvement of an emotion classification model with a higher accuracy, less cost, and that is less time consuming, which has the potential to be further applied to various areas of applications.

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Kansei values are critical factors for manufacturing. Kawaii, an adjective that denotes such positive meanings as cute or lovable, has become even more critical as a kansei value. Therefore, we believe that kawaii will be a key factor in future product design. We focus on systematic study for kawaii products in terms of such physical attributes as color, shape, and material perception. In this research, we proposed a method to obtain effective attributes for kawaii cosmetic bottles. First, we collected evaluation results of the kawaii of cosmetic bottle images and constructed a model using deep learning. Then we modified the images focusing on particular attributes and evaluated them using our model to obtain effective attributes. Finally, we verified those attributes by experimentally evaluating the modified images. The novelty of this research is our new method to obtain effective attributes, which can be applied to other products and other kansei values.