As many psychologists have suggested, evoking memories of gratitude and then writing them down is recognized as an efficient daily activity to improve people's subjective happiness and well-being. This paper studies how using a smart voice agent, such as a smartphone and smart speaker, affects this activity. Instead of traditional handwriting, expressing gratitude through the voice agent has several benefits in terms of human-computer interaction aspects. As a first step for this study, we conducted short-term experiments using a voice agent with Japanese university students, including native Japanese and foreigners, and evaluated changes in their emotional state by using two psychological measurements, the Positive and Negative Affect Schedule (PANAS) and the Subjective Happiness Scale (SHS). The evaluation results revealed three important findings: (1) expressing and receiving gratitude via voice agent can potentially enhance positive affect and subjective happiness similar to traditional handwriting, (2) generating natural gratitude messages (e.g., from close friends or family) significantly improves positive affect and happiness, and (3) some people experience stress over tasks or methods of expressing gratitude, decreasing positive affect.

Developing a new IoT device input method that can reduce the burden on users has become an important issue. This paper proposed a system Stetho Touch that identifies touch actions using acoustic information obtained when a user's finger makes contact with a solid object. To investigate the method, we implemented a prototype of an acoustic sensing device consisting of a low-pressure melamine veneer table, a stethoscope, and an audio interface. The CNN-LSTM classification model of combining CNN and LSTM classified the five touch actions with accuracy 88.26%, f-score 87.26% in LOSO and accuracy 99.39, f-score 99.39 in 18-fold cross-validation. The contributions of this paper are the following; (1) proposed a touch action recognition method using acoustic information that is more natural and accurate than existing methods, (2) evaluated a touch action recognition method using Deep Learning that can be processed in real-time using acoustic time series raw data as input, and (3) proved the compensations for the user dependence of touch actions by providing a learning phase or performing sequential learning during use.