Mobile solutions can help transform speech and sound into visual representations for people who are deaf or hard-of-hearing (DHH). However, where handheld phones present challenges, head-worn displays (HWDs) could further communication through privately transcribed text, hands-free use, improved mobility, and socially acceptable interactions.

To investigate preferences for mobile and wearable sound awareness systems, we conducted an online survey with 201 DHH participants. The survey explores how demographic factors affect perceptions of sound awareness technologies, gauges interest in specific sounds and sound characteristics, solicits reactions to three design scenarios (smartphone, smartwatch, head-mounted display) and two output modalities (visual, haptic), and probes issues related to social context of use. While most participants were highly interested in being aware of sounds, this interest was modulated by communication preference--that is, for sign or oral communication or both. Almost all participants wanted both visual and haptic feedback and 75% preferred to have that feedback on separate devices (e.g., haptic on smartwatch, visual on head-mounted display). Other findings related to sound type, full captions vs. keywords, sound filtering, notification styles, and social context provide direct guidance for the design of future mobile and wearable sound awareness systems.

Exercise plans help people implement behavior change. Crowd workers can help create exercise plans for clients, but their work may result in lower quality plans than produced by experts. We built CrowdFit, a tool that provides feedback about compliance with exercise guidelines and leverages strengths of crowdsourcing to create plans made by non-experts. We evaluated CrowdFit in a comparative study with 46 clients using exercise plans for two weeks. Clients received plans from crowd planners using CrowdFit, crowd planners without CrowdFit, or from expert planners. Compared to crowd planners not using CrowdFit, crowd planners using CrowdFit created plans that are more actionable and more aligned with exercise guidelines. Compared to experts, crowd planners created more actionable plans, and plans that are not significantly different with respect to tailoring, strength and aerobic principles. They struggled, however, to satisfy exercise requirements of amount of exercise. We discuss opportunities for designing technology supporting physical activity planning by non-experts.

Distance Cartograms (DC) distort geographical features so that the measured distance between a single location and any other location on a map indicates absolute travel time. Although studies show that users can efficiently assess travel time with DC, distortion applied in DC may confuse users, and its usefulness "in the wild" is unknown. To understand how real world users perceive DC's benefits and drawbacks, we devise techniques that improve DC's presentation (preserving topological relationships among map features while aiming at retaining shapes) and scalability (presenting accurate live travel time). We developed a DC-enabled system with these techniques, and deployed it to 20 participants for 4 weeks. During this period, participants spent, on average, more than 50% of their time with DC as opposed to a standard map. Participants felt DC to be intuitive and useful for assessing travel time. They indicated intent in adopting DC in their real-life scenarios.

Collaborative qualitative coding often involves coders assign- ing different labels to the same instance, leading to ambiguity. We refer to such an instance of ambiguity as disagreement in coding. Analyzing reasons for such a disagreement is essential-- both for purposes of bolstering user understanding gained from coding and reinterpreting the data collaboratively, and for negotiating user-assigned labels for building effective machine learning models. We propose a conceptual definition of collective disagreement using diversity and divergence within the coding distributions. This perspective of disagreement translates to diverse coding contexts and groups of coders irrespective of discipline. We introduce two tree-based ranking metrics as standardized ways of comparing disagreements in how data instances have been coded. We empirically validate that, of the two tree-based metrics, coders' perceptions of dis- agreement match more closely with the n-ary tree metric than with the post-traversal tree metric.