Primary symptoms of adults with autism spectrum disorder (ASD), such as pervasive social deficits in social interaction and communication, cause adults with ASD to adopt a sedentary lifestyle. Meanwhile, gamified and behavioral theory-based interventions have been shown to improve physical activity in a fun and unobtrusive way. In this paper, we describe the iterative design inquiry process of PuzzleWalk, a gamified, physical activity-promoting mobile app designed for adults with ASD. We report the design rationales and lessons learned across four user-centered design phases with ASD experts and adults with ASD, including user requirement gathering, iterative participatory design, usability evaluation, and field deployment. The design insights generated from this work could inform future research focusing on designing sociotechnical systems, games, and interventions for people with ASD.

A virtual reality (VR) controller plays a key role in supporting interactions between users and the virtual environment. This paper investigates the relationship between the user experience and VR control device modality. We developed a VR firefighting training system integrated with four control devices adapted from real firefighting tools. We iteratively improved the controllers and VR system through a pilot study with six participants and conducted a user study with 30 participants to assess two salient human factor constructs—perceived presence and cognitive load—with three device modality conditions (two standard VR controllers, four real tools, and a hybrid of one real tool and one standard VR controller). We found that having more realistic devices that simulate real tools does not necessarily guarantee a higher level of user experience, highlighting a strategic approach to the development and utilization of VR control devices. Our study gives empirical insights on establishing appropriate combinations of VR control device modality in the context of field-based VR simulation and training.

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A user's movement is one of the most important properties that pertain to user experience in a virtual reality (VR) environment. However, little research has focused on examining backward movements. Inappropriate support of such movements could lead to dizziness and disengagement in a VR program. In this paper, we investigate the possibility of detecting forward and backward movements from three different positions of the body (i.e., head, waist, and feet) by conducting a user study. Our machine-learning model yields the detection of forward and backward movements up to 93% accuracy and shows slightly varying performances by the participants. We detail the analysis of our model through the lenses of body position, integration, and sampling rate. CCS CONCEPTS • Human-centered computing → Virtual reality.