Multiple embedded sensors enable smartwatch apps to amass large amounts of interrelated time-series data simultaneously, such as heart rate, oxygen levels or steps walked. Visualizing multiple interlinked datasets is possible on smartphones but remains challenging on small smartwatch displays. We propose a new technique, the Space-Filling Line Graph (SF-LG), that preserves the key visual properties of time-series graphs while making available space on the display to augment such graphs with additional information. Results from our first study (N=30) suggest that, while SF-LG makes available additional space on the small display, it also enables effective (i.e. quick and accurate) comprehension of key line graph tasks. We next implement a greedy algorithm to embed auxiliary information in the most suitable regions on the display. In a second study (N=27), we find that participants are efficient at locating and linking interrelated content using SF-LG in comparison to two baselines approaches. We conclude with guidelines for smartwatch space maximization for visual displays.

The use of crowdsourcing platforms for data collection in HCI research is attractive in their ability to provide rapid access to large and diverse participant samples. As a result, several researchers have conducted studies investigating the similarities and differences between data collected through crowdsourcing and more traditional, laboratory-style data collection. We add to this body of research by examining the feasibility of conducting social acceptability studies via crowdsourcing. Social acceptability can be a key determinant for the early adoption of emerging technologies, and as such, we focus our investigation on social acceptability for Head-Worn Display (HWD) input modalities. Our results indicate that data collected via a crowdsourced experiment and a laboratory-style setting did not differ at a statistically significant level. These results provide initial support for crowdsourcing platforms as viable options for conducting social acceptability research.

The popularity of head-worn displays (HWD) technologies such as Virtual Reality (VR) and Augmented Reality (AR) headsets is growing rapidly. To predict their commercial success, it is essential to understand the acceptability of these new technologies, along with new methods to interact with them. In this vein, the evaluation of social acceptability of interactions with these technologies has received significant attention, particularly from the performer's (i.e., user's) viewpoint. However, little work has considered social acceptability concerns from observers' (i.e., spectators') perspective. Although HWDs are designed to be personal devices, interacting with their interfaces are often quite noticeable, making them an ideal platform to contrast performer and observer perspectives on social acceptability. Through two studies, this paper contrasts performers' and observers' perspectives of social acceptability interactions with HWDs under different social contexts. Results indicate similarities as well as differences, in acceptability, and advocate for the importance of including both perspectives when exploring social acceptability of emerging technologies. We provide guidelines for understanding social acceptability specifically from the observers' perspective, thus complementing our current practices used for understanding the acceptability of interacting with these devices.

The decision making process is usually one of the most critical elements of any face-to-face meeting. Participants in a group meeting follow certain procedures and guidelines for facilitating the decision making process. These include such rules as turn taking, not interrupting the consultation, and keeping suggestions or ideas clear and concise. The ultimate objective of such meetings is to arrive at decisions in a timely manner by having a diversity of opinions from as many participants as possible. We postulate that by visualizing the group dynamics during a face-to-face meeting, administrators might be able to get a better handle on the strengths and weaknesses of the group's consultation process. The end result would be to improve the overall quality of meetings. We present a visualization tool that captures and reveals the ongoing social dynamics during the decision-making process within a face-to-face and real-time meeting. Our system captures the required data through a simple and easy-to-use interface and then visualizes the outcome of the meeting.