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Artificial Intelligence (AI)-driven Digital Health (DH) systems are poised to play a critical role in the future of healthcare. In 2021, $57.2 billion was invested in DH systems around the world, recognizing the promise this concept holds for aiding in delivery and care management. DH systems traditionally include a blend of various technologies, AI, and physiological biomarkers and have shown a potential to provide support for individuals with various health conditions. Digital therapeutics (DTx) is a more specific set of technology-enabled interventions within the broader DH sphere intended to produce a measurable therapeutic effect. DTx tools can empower both patients and healthcare providers, informing the course of treatment through data-driven interventions while collecting data in real-time and potentially reducing the number of patient office visits needed. In particular, socially assistive robots (SARs), as a DTx tool, can be a beneficial asset to DH systems since data gathered from sensors onboard the robot can help identify in-home behaviors, activity patterns, and health status of patients remotely. Furthermore, linking the robotic sensor data to other DH system components, and enabling SAR to function as part of an Internet of Things (IoT) ecosystem, can create a broader picture of patient health outcomes. The main challenge with DTx, and DH systems in general, is that the sheer volume and limited oversight of different DH systems and DTxs is hindering validation efforts (from technical, clinical, system, and privacy standpoints) and consequently slowing widespread adoption of these treatment tools. 

Social robot co-design requires aiding users as they imagine these novel devices within their everyday lives and enabling designers to understand and address users’ experiences. This paper presents the exploratory development and evaluation of a role-playing game aimed at identifying the desired features and uses of a social robot that can assist people diagnosed with depression. Participants (n = 16) played the game as a character with depression, designed a companion robot for that character, and chose reactions to daily challenges. Though participants initially selected robot capabilities based on their own needs, after the game they identified alternative designs that would better address daily challenges faced by individuals with depression. We discuss aspects of the game that allowed participants to understand how various robot characteristics can address the experience of depression and suggest how role-playing games can support users and designers in identifying beneficial features and uses of emerging robotic technologies. 

This paper presents an intensive case study of 10 participants in the US and South Korea interacting with a robotic companion pet in their own homes over the course of several weeks. Participants were tracked every second of every day during that period of time. The fundamental goal was to determine whether there were significant differences in the types of interactions that occurred across those cultural settings, and how those differences affected modeling of the human-robot interactions. We collected a mix of quantitative and qualitative data through sensors onboard the robot, ecological momentary assessment (EMA), and participant interviews. Results showed that there were significant differences in how participants in Korea interacted with the robotic pet relative to participants in the US, which impacted machine learning and deep learning models of the interactions. Moreover, those differences were connected to differences in participant perceptions of the robot based on the qualitative interviews. The work here suggests that it may be necessary to develop culturally-specific models and/or sensor suites for human-robot interaction (HRI) in the future, and that simply adapting the same robot's behavior through cultural homophily may be insufficient. 

The last several years have seen a strong growth of telerobotic tech- nologies with promising implications for many areas of learning. HCI has contributed to these discussions, mainly with studies on user experiences and user interfaces of telepresence robots. How- ever, only a few telerobot studies have addressed everyday use in real-world learning environments. In the post-COVID 19 world, sociotechnical uncertainties and unforeseen challenges to learning in hybrid learning environments constitute a unique frontier where robotic and immersive technologies can mediate learning experi- ences. The aim of this workshop is to set the stage for a new wave of HCI research that accounts for and begins to develop new in- sights, concepts, and methods for use of immersive and telerobotic technologies in real-world learning environments. Participants are invited to collaboratively defne an HCI research agenda focused on robot-mediated learning in the wild, which will require exam- ining end-user engagements and questioning underlying concepts regarding telerobots for learning.