Meng-Han Wu’s research while affiliated with Purdue University West Lafayette and other places

Prototyping use cases for Augmented Reality (AR) applications can be beneficial to elicit the functional requirements of the features early-on, to drive the subsequent development in a goal-oriented manner. Doing so would require designers to identify the goal-oriented interactions and map the associations between those interactions in a spatio-temporal context. Pertaining to the multiple scenarios that may result from the mapping, and the embodied nature of the interaction components, recent AR prototyping methods lack the support to adequately capture and communicate the intent of designers and stakeholders during this process. We present ImpersonatAR, a mobile-device based prototyping tool that utilizes embodied demonstrations in the augmented environment to support prototyping and evaluation of multi-scenario AR use cases. The approach uses: 1) capturing events or steps in form of embodied demonstrations using avatars and 3D animations, 2) organizing events and steps to compose multi-scenario experience, and finally 3) allowing stakeholders to explore the scenarios through interactive role-play with the prototypes. We conducted a user study with 10 participants to prototype use cases using ImpersonatAR from two different AR application features. Results validated that ImpersonatAR promotes exploration and evaluation of diverse design possibilities of multi-scenario AR use cases through embodied representations of the different scenarios.

Developing instructions for microtask crowd workers requires time to ensure consistent interpretations by crowd workers. Even with substantial effort, workers may still misinterpret the instructions due to ambiguous language and structure in the task design. Prior work demonstrated methods for facilitating iterative improvement with help from the requester. However, any participation by the requester reduces the time saved by delegating the work—and hence the utility of using crowdsourcing. We present TaskMate, a system for facilitating worker-led refinement of task instructions with minimal involvement by the requester. Small teams of workers search for ambiguities and vote on the interpretation they believe the requester intended. This paper describes the workflow, our implementation, and our preliminary evaluation.