Museum artifacts are the main way for visitors to experience and learn about cultural heritage. Augmented reality (AR) allows for high interactivity and is increasingly applied in museums to improve tourists’ experience and learning. It also supports the extension of museum experience to outside of the physical museum space, contributing to the visiting trajectory and takeaway experience. In this paper, we present our design of two tangible AR interfaces for cultural artifacts: Postcard AR and CubeMuseum AR, followed by three user studies that evaluate and optimize the design. In Study 1, we conducted a within-subjects study (N = 24) that compares the two AR interfaces with a baseline condition (Leaflet). Our results demonstrate the positive effects of tangible AR interfaces on users’ motivation and engagement in learning cultural heritage. In Study 2, we further explored how to optimize CubeMuseum AR by adopting a user-centered design approach. Through the analysis of expert interviews (N = 7) and an online survey (N = 207), the results specify a series of requirements and design guidelines for tangible AR interfaces to be used as a learning tool and a hybrid gift. Based on the findings, the design of the CubeMuseum AR was optimized and evaluated in Study 3. A between-subjects user study was conducted (N = 32) to compare the optimized design with the initial design. The results verified the positive effects of gamified tangible AR interfaces on users’ motivation, engagement, and performance in learning cultural heritage. We present our design and evaluation results, and discuss the implications of designing tangible AR interfaces for cultural heritage learning and museum gifting.

Target selection is a fundamental task in interactive Augmented Reality (AR) systems. Predicting the intended target of selection in such systems can provide users with a smooth, low-friction interaction experience. Our work aims to predict gaze-based target selection in AR headsets with eye and head endpoint distributions, which describe the probability distribution of eye and head 3D orientation when a user triggers a selection input. We first conducted a user study to collect users’ eye and head behavior in a gaze-based pointing selection task with two confirmation mechanisms (air tap and blinking). Based on the study results, we then built two models: a unimodal model using only eye endpoints and a multimodal model using both eye and head endpoints. Results from a second user study showed that the pointing accuracy is improved by approximately 32% after integrating our models into gaze-based selection techniques.

Target selection in dense virtual reality (VR) environments is challenging. Prior work has explored different controller-based raycasting techniques to assist target selection in such environments. However, limited research has focused on selection via mid-air barehand, which represents another major input metaphor for immersive environments. In this paper, we first review the existing raycasting selection techniques for dense VR environments. Based on this, we propose and develop two freehand pointing selection techniques—HandDepthCursor and HandConeGrid, and implement MultiFingerBubble, a recently-proposed technique. We then conduct a user study to compare and evaluate their performance and experience in a target selection task in dense VR environments. Our results suggest that HandDepthCursor and HandConeGrid led to significantly faster and more accurate selection performance, and lower perceived workload and arm fatigue. In addition, HandConeGrid showed a distinct advantage in high-density environments.

In recent years, with growing health consciousness, at-home workout has become increasingly popular for its convenience and safety. Most people choose to follow video guidance during exercising. However, our preliminary study revealed that fitness-minded people face challenges when watching exercise videos on handheld devices or fixed monitors, such as limited movement comprehension due to static camera angles and insufficient feedback. To address these issues, we reviewed popular workout videos, identified user requirements, and came up with an augmented reality (AR) solution. Following a user-centered iterative design process, we proposed a design space of AR visual cues for workouts and implemented an AR-based application. Specifically, we captured users’ exercise performance with pose-tracking technology and provided feedback via AR visual cues. Two user experiments showed that incorporating AR visual cues could improve movement comprehension and enable users to adjust their movements based on real-time feedback. Finally, we presented several suggestions to inspire future design and apply AR visual cues to sports training.