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The prevalence of functional and affordable wearable biosensors has led to an increase in stress management applications for learning about and improving one’s mental health. Previous research suggests that different types of graphical interfaces may evoke diverse user experiences with biofeedback interpretations and related interventions. In this research, we systematically investigate the effect of various biofeedback visual designs on supporting user engagement during a guided stress management practice. Specifically, we hypothesize that certain visual designs will affect user engagement and, thus, the training attention and physiological measures, due to their specific design features. Following a series of design studies, we assess five different styles of visual design and, for each style, we identify a suitable representative (i.e., raw numbers for the numerical style, bar charts for the analytical style, waves for the illustrative style, butterfly drawings for the artistic style, and table lamps for the ambient style). In a controlled within-subject experiment with 35 participants, we assess these five designs through a biofeedback-assisted stress management practice. We find that, overall, visual representation of data increases engagement and, thus, better facilitates stress management training when compared to a numerical display. The analysis through the results suggests that a certain level of abstraction in the interpretation of data encoding helps users move away from being judgmental; whereas animations and transitions augment the experience of being in the present moment.

Despite the popularity of photo editors used to improve image attractiveness and expressiveness on social media, many users have trouble making sense of color filter effects and locating a preferred filter among a set of designer-crafted candidates. The problem gets worse when more computer-generated filters are introduced. To enhance filter findability, we semantically name and organize color effects leveraging data curated by creative communities online. We first model semantic mappings between color themes and keywords in everyday language. Next, we index and organize each filter by the derived semantic information. We conduct three separate studies to investigate the benefit of the semantic features on filter exploration. Our results indicate that color theme semantics constructed through social curation enhances filter findability, providing important implications into how to use the wisdom of the crowd to improve user experience with image editors.