Tracking for people living with dementia and their care partners is primarily focused on quantified dementia symptoms presented to care partners. However, what people living with dementia want to track, what other aspects of dementia care partners wish to know, and how tracking fits within the care relationship remain to be identified. We performed an exploratory study in which eight people living with dementia and nine care partners provided iterative design feedback on a system concept: one that captures conversational data from a robot and visualizes it through a speech-tracking mobile application. Through reflexive thematic analysis, we found that people living with dementia wanted to use the system to maintain autonomy, especially by talking about their symptoms with the robot and using tracked information as a memory aid. Care partners valued numerical insights into the cognitive progress of people living with dementia only when accompanied by clear calls to action that supported them in their caregiver roles. Simultaneously, in their relational roles as spouses or children, care partners valued tracking memories and discussion points to understand their loved ones better. Our results suggest that providing related but distinct information tailored to each user’s needs can support both in their care relationship.

There is great interest in applying artificial intelligence (AI) techniques to healthcare issues such as Autism, particularly in combination with digital health technologies (robots, wearables, smartphones, etc.) in user homes. However, a critical challenge is that modern AI techniques like deep learning (DL) typically require large datasets with millions of samples, yet in healthcare we are often working with smaller clinical samples (<50 participants). To address that challenge, we need to develop new approaches that can learn more efficiently from less data. In this paper, we propose a novel approach to few-shot learning (FSL) called SMOTE_FSL, which is applicable to traditional machine learning (ML) models, allowing them to work with smaller sample sizes as well as various types of healthcare data (not only image or text data). We compare SMOTE_FSL on two healthcare sensor datasets gathered using robots and wearables, with results showing SMOTE_FSL performs comparably to state-of-the-art DL-based FSL methods (e.g. autoencoders, generative adversarial networks [GAN]). That indicates such an approach holds potential to expand utilization of FSL to a broad range of healthcare data derived from smaller clinical sample sizes.

Socially assistive robots can be used as therapeutic technologies to address depression symptoms. Through three sets of workshops with individuals living with depression and clinicians, we developed design guidelines for a personalized therapeutic robot for adults living with depression. Building on the design of Therabot, workshop participants discussed various aspects of the robot's design, sensors, behaviors, and a robot connected mobile phone app. Similarities among participants and workshops included a preference for a soft textured exterior and natural colors and sounds. There were also differences - clinicians wanted the robot to be able to call for aid, while participants with depression differed in their degree of comfort in sharing data collected by the robot with clinicians.

Using socially assistive robots (SARs) as specialized companions for those living with depression to manage symptoms provides a unique opportunity for exploration of robotic systems as comfort objects. Moreover, the robotic components allow for specialized behavioral responses to particular stimuli, as preferred by the user. We have conducted semi-structured interviews with 10 participants about the zoomorphic robot’s Therabot™ desired behaviors and focus groups with five additional participants regarding the preferred sensors within the Therabot™ system. In this paper, using the data from interviews and focus groups, we explore SAR input and output for depression management. While participants overall expected the robot to respond in much similar ways as a well-trained service animal, they expressed interest in the robot understanding unique information about the environment and the user, such as when the user might need interaction.

Socially-assistive robots (SARs) hold significant potential to transform the management of chronic healthcare conditions (e.g. diabetes, Alzheimer’s, dementia) outside the clinic walls. However doing so entails embedding such autonomous robots into people’s daily lives and home living environments, which are deeply shaped by the cultural and geographic locations within which they are situated. That begs the question whether we can design autonomous interactive behaviors between SARs and humans based on universal machine learning (ML) and deep learning (DL) models of robotic sensor data that would work across such diverse environments? To investigate this, we conducted a long-term user study with 26 participants across two diverse locations (United States and South Korea) with SARs deployed in each user’s home for several weeks. We collected robotic sensor data every second of every day, combined with sophisticated ecological momentary assessment (EMA) sampling techniques, to generate a large-scale dataset of over 270 million data points representing 173 hours of randomly-sampled naturalistic interaction data between the human and SAR. Models built on that data were capable of achieving nearly 84% accuracy for detecting specific interaction modalities (AUC 0.885) when trained/tested on the same location, though suffered significant performance drops when applied to a different location. Further analysis and participant interviews showed that was likely due to differences in home living environments in the US and Korea. The results suggest that our ability to create adaptable behaviors for robotic pets may be dependent on the human-robot interaction (HRI) data available for modeling.

Socially-Assistive Robots (SARs) hold great potential to revolutionize the way we manage chronic illness outside clinical settings, but a current limitation to their broad adoption for this purpose is the lack of "ground truth" around interactions between robots and humans in in-home settings.Such ground truth is a necessity for using robotic sensor data for machine learning models of patient activity patterns or to create AI to customize robotic interactive behavior autonomously.Traditional subjective recall-based data collection methods lack the fine-grained temporal detail to support such AI development, as well as suffering from "recall bias" effects.One potential solution to this challenge is to adapt novel forms of interaction assessment, such as ecological momentary assessment (EMA), to collect patient interaction data in real-time.Here we describe a pilot study utilizing such an EMA system with SARs.We describe the development of the EMA framework, theoretical design issues, and lessons learned.Preliminary machine learning results indicate 75-80% accuracy for detecting specific interaction modalities.We also discuss the potential utility of EMA for exploring cross-cultural differences with in-the-wild robot use, and as a tool to support participatory design research on robotics in healthcare settings.