The rise in popularity and value of esports motivates the creation of computational training tools (CTTs) for learning, assessment, and skill gain. While some tools exist commercially, much of the work in the research literature is rarely used outside of a lab, resulting in a lack of knowledge on the challenges involved in real-world integration. In this work, we develop a bespoke CTT for League of Legends—MySkills—based on prior work and deploy it at a professional training academy for three months. Based on two rounds of stakeholder interviews, we uncover insights into users’ perspectives on using CTTs in esports coaching and the challenges inherent in introducing a novel tool into an existing, real-world esports training context. From these results, we connect the domain of esports training technology to existing conversations on translational HCI, challenges in bridging research and practice, and present implications for future work.

To achieve high performance in esports, players must be able to effectively and efficiently control input devices such as a computer mouse and keyboard (i.e., input skills). Characterizing and quantifying a player’s input skills can provide useful insights, but collecting and analyzing sufficient amounts of data in ecologically valid settings remains a challenge. Targeting the popular esports game, League of Legends, we go beyond the limitations of previous studies and demonstrate a holistic pipeline of input behavior analysis: from quantifying the quality of players’ input behavior (i.e., input skill) to training players based on the analysis. Based on interviews with five top-tier professionals and analysis of input behavior logs from 4,835 matches played freely at home collected from 193 players (including 18 professionals), we confirmed that players with higher ranks in the game implement eight different input skills with higher quality. In a three-week follow-up study using a training aid that visualizes a player’s input skill levels, we found that the analysis provided players with actionable lessons, potentially leading to meaningful changes in their input behavior.

Smart speakers have different speech style depending on the installed artificial intelligence (AI). Furthermore, the AI agent’s appearances can make different impressions. Hence, it might give characters to the AI agent through speaking and appearance modalities. To determine how an AI agent’s speech and tactility types affect users’ perception, we designed a 2(speech types: assistant-like vs. companion-like) × 2(flexibility types: flexible vs. hard) × 2(roughness types: rough vs. smooth) mixed-participant experiment (N=48). As a result, when the speech style is like a companion, it is possible to give an impression of sociability through a flexible material. However, there was no significant difference by flexibility type when being an assistant. In addition, there was a significant interaction effect between speech types and flexibility types on usefulness. When the speech type is companion-like, the AI agent with flexible material was perceived as being more useful and providing better services than that with hard material. On the contrary, when the speech type is assistant-like, the opposite result was revealed. Regardless of the speech type, a flexible material increases the impression of sociability with higher service evaluation, and a rough finish gives the AI agent an impression of usefulness with higher service evaluation.

The purpose of this study is to investigate a robot's impression perceived by users as well as the accuracy of perception of location information, which the robot provided according to the modality type of the robot. To explore this, we designed two 2 (verbal types: deictic vs. descriptive) x 2 (nose pointing: with nose vs. without nose) x 2 (eye pointing: with eyes vs. without eyes) mixed-participant studies. In the first study, we investigated the impacts of the robot's modality type in the imperative pointing situation. As a result, participants identified the robot's pointing gesture with nose as more effective, social, and positive, than the robot's pointing gesture without nose. Moreover, the descriptive speech robot was evaluated as more positive than the deictic speech robot. In terms of the accuracy of perception of location information, which the robot provided, participants identified the robot-designated chair more accurately when the robot delivered a deictic speech than when the robot delivered a descriptive speech. For the second study, we explored the effects of the robot's modality type in the declarative pointing situation. As a result, the robot's descriptive speech was rated as effective, social, natural, competent, trustworthy, and more positive than deictic speech. In the case of the robot's pointing gestures, pointing gesture with nose was evaluated as more effective, social, natural, competent, trustworthy, and positive than that without nose. In terms of the accuracy of location information perception, participants perceived the location of the object designated by the robot more accurately when the robot used descriptive speech, pointed with nose and without eyes.

With the development of robotic technology, various types of robotic products have been developed, ranging from robotic objects that support small daily necessities (such as robotic umbrellas and frames) to robotic furniture (such as robotic chairs and drawers). Owing to consumerism, the consumer marketplace now overflows with surplus products, and storage and organizational products have been increasingly necessary. With this social stream, we focused on designing robotic storage furniture in this study. To create such a type of furniture that is acceptable by consumers, a qualitative user study was conducted with 16 subjects by analyzing the users' behaviors when using ordinary storage furniture. From this user study, we found strong user needs in terms of organizing and finding objects. Thus, we developed two types of robotic cabinets: the first one to assist users' organizing behavior and the second one to assist their finding behavior. To examine the effectiveness of the developed prototypes, we executed a 3 (behavior the robotic cabinet assists: baseline vs. organizing vs. finding) within-participants experiment. The result shows that significant effects vary depending on the type of robotic cabinet.

Robotic things and social robots have been introduced into home, and they are expected to change the relationships between humans. Our study examines whether the introduction of robotic things or social robots, and the way that they are organized, can change the social relationship between family members. To observe this phenomenon, we designed a living lab experiment that simulated a home environment and recruited two families to participate. Families were asked to conduct home activities within two different types of Internet of Robotic Things(IoRT):1)internet of only robotic things(IoRT without mediator condition), and 2)internet of robotic things mediated by a social robot(IoRT with mediator condition). We recorded the interactions between the family members and the robotic things during the experiments and coded them into a dataset for social network analysis. The results revealed relationship differences between the two conditions. The introduction of IoRT without mediator motivated younger generation family members to share the burden of caring for other members, which was previously the duty of the mothers. However, this made the interaction network inefficient to do indirect interaction. On the contrary, introducing IoRT with mediator did not significantly change family relationships at the actor-level, and the mothers remained in charge of caring for other family members. However, IoRT with mediator made indirect interactions within the network more efficient. Furthermore, the role of the social robot mediator overlapped with that of the mothers. This shows that a social robot mediator can help the mothers care for other members of the family by operating and managing robotic things. Additionally, we discussed the implications for developing the IoRT for home.