In Affective computing, recognizing users' emotions accurately is the basis of affective human-computer interaction. Understanding users' interoception contributes to a better understanding of individually different emotional abilities, which is essential for achieving inter-individually accurate emotion estimation. However, existing interoception measurement methods, such as the heart rate discrimination task, have several limitations, including their dependence on a well-controlled laboratory environment and precision apparatus, making monitoring users' interoception challenging. This study aims to determine other forms of data that can explain users' interoceptive or similar states in their real-world lives and propose a novel hypothetical concept "cyberoception," a new sense (1) which has properties similar to interoception in terms of the correlation with other emotion-related abilities, and (2) which can be measured only by the sensors embedded inside commodity smartphone devices in users' daily lives. Results from a 10-day-long in-lab/in-the-wild hybrid experiment reveal a specific cyberoception type "Turn On" (users' subjective sensory perception about the frequency of turning-on behavior on their smartphones), significantly related to participants' emotional valence. We anticipate that cyberoception to serve as a fundamental building block for developing more "emotion-aware", user-friendly applications and services.

Remote communication is challenging compared with face-to-face communication because of the limited social cues. Computer systems built to address this problem could enable individuals to share facial expressions, which are available in face-to-face communication, or biosignals such as heart rate. Currently, many people are unfamiliar with the advantages of sharing such signals online, and clarification of the associated benefits is necessary. To compare the effects of heart rate and facial expression data on the ease of communication, we exposed 20 pairs of participants to a remote work simulation. In this experiment, one participant made contact with their remote partner on the basis of heart rate or facial expression data. Although participants evaluated the heart rate and facial expression data as being similarly useful on average, we found the following differences. Recipients of the contact evaluated the heart rate data as more effective, likely because the recipient's status was more accurately discriminated by the sender. Senders evaluated the facial expression data as more effective, likely because the many parameters were easily interpreted and quickly updated. Thus, heart rate and facial expression data may increase the ease of remote communication, particularly for the recipient and the sender, respectively.

Sustained input of lifelog data is critical for conversational health applications, where algorithms and AI advise users based on recorded lifelog data such as meals, exercise, and sleep. However, an effective method for presenting information to encourage this continuity has not been identified. This study developed three intervention methods for prompting lifelog entries: (a) wording adjustment based on individual characteristics, (b) timing adjustment based on physical activity, and (c) a combination of these adjustments. An empirical experiment with 422 participants was conducted to evaluate the effects.

Many researchers and companies have engaged in estimating users' interests so that an online shopping system can tell what he/she wants now. This paper tackles the next challenge in online shopping, i.e., predicting the times that users go shopping online. To predict the timing of online shopping, we focus on "wandering behavior" in web search activities and propose a "search wandering score" (SWS). Online shopping behavior can be categorized into three states: "wandering shop-ping", "focused shopping", and others. Wandering shopping is a state in which users make purchases in high SWS situations; focused shopping is a state in which users buy things in low SWS situations. Unlike previous studies, our work is based on an analysis of large-scale data containing shopping and search logs produced by approximately 200,000 users of a real web portal site for over a year. The results of an extensive evaluation show that our methodology can predict user's future shopping behavior types with 86% accuracy. This research is the first step towards understanding the relationship between users' mental states and their online shopping behavior.

We propose "SmileWave", the first selfie social networking service to reveal the existence of emotional cognation through smiling selfies on the social network. We conducted multiple rounds of in-the-wild user studies with 86 cumulative total users for total duration of 5 weeks. Throughout the entire study, we confirmed the occurrence of smile-based emotional contagion over social network, not only in the momentary duration but in longer term period.

This paper proposes ''SmileWave", a system for revealing smile-based emotional contagion, propagation effect of the similar emotion through smiley facial expression, on the social network where users interact each other through web-based user interface rather than in-person interaction. SmileWave is a picture-based networking service and detects the change of smile degree when the user looks at posted smile images of others. Our extensive user study with 50 participants for 30 days confirmed the emotional contagion effect on SmileWave. Users' smile degree improved by 27% when the user looked at posted smile images. The result also proved that there is a stronger effect on smile-based emotional contagion when the examinee and the person in the image are in close relationship.