Recommending fashion outfits to users presents several challenges. First of all, an outfit consists of multiple fashion items, and each user emphasizes different parts of an outfit when considering whether they like it or not. Secondly, a user’s liking for a fashion outfit considers not only the aesthetics of each item but also the compatibility among them. Lastly, fashion outfit data is often sparse in terms of the relationship between users and fashion outfits. Not to mention, we can only obtain what the users like, but not what they dislike.

Over 22 million people worldwide are affected by Parkinson's disease, stroke, and Bell's palsy (BP), which can cause facial paralysis (FP). People with FP have trouble having their expressions understood: both laypersons and clinicians have difficulty understanding them and often misinterpret them, which can result in poor social interactions and poor care delivery. One way to address this problem is through better education and training, of which computational tools may prove invaluable. Thus, in this paper, we explore how to build systems that can recognize and synthesize asymmetrical facial expressions. We introduce a novel computational model of asymmetric facial expressions for BP, which we can synthesize on either virtual and robotic patient simulators. We explore this within the context of clinical education, and built a patient simulator with synthesized FP in order to help clinicians perceive facial paralysis in patients. We conducted both computational and human-focused evaluations of the model, including the feedback from clinical experts. Our results suggest that our BP model is realistic, and comparable to the expressions of people with BP. Thus, this work has the potential to provide a practical training tool for clinical learners to better understand the expressions of people with BP. Our work can also help researchers in the facial recognition community to explore new methods for asymmetric facial expression analysis and synthesis.

In this paper, we introduce a novel application of social robotics in healthcare: high fidelity, facially expressive, robotic patient simulators (RPSs), and explore their usage within a clinical experimental context. Current commercially-available RPSs, the most commonly used humanoid robots worldwide, are substantially limited in their usability and fidelity due to the fact that they lack one of the most important clinical interaction and diagnostic tools: an expressive face. Using autonomous facial synthesis techniques, we synthesized pain both on a humanoid robot and comparable virtual avatar. We conducted an experiment with 51 clinicians and 51 laypersons (n = 102), to explore differences in pain perception across the two groups, and also to explore the effects of embodiment (robot or avatar) on pain perception. Our results suggest that clinicians have lower overall accuracy in detecting synthesized pain in comparison to lay participants. We also found that all participants are overall less accurate detecting pain from a humanoid robot in comparison to a comparable virtual avatar, lending support to other recent findings in the HRI community. This research ultimately reveals new insights into the use of RPSs as a training tool for calibrating clinicians' pain detection skills.

As robots enter human environments, they will be expected to accomplish a tremendous range of tasks. It is not feasible for robot designers to pre-program these behaviors or know them in advance, so one way to address this is through end-user programming, such as learning from demonstration (LfD). While significant work has been done on the mechanics of enabling robot learning from human teachers, one unexplored aspect is enabling mutual feedback between both the human teacher and robot during the learning process, i.e., implicit learning. In this paper, we explore one aspect of this mutual understanding, grounding sequences, where both a human and robot provide non-verbal feedback to signify their mutual understanding during interaction. We conducted a study where people taught an autonomous humanoid robot a dance, and performed gesture analysis to measure people's responses to the robot during correct and incorrect demonstrations.