NotiBike: Assessing Target Selection Techniques for Cyclist Notifications in Augmented Reality

Abstract

Cyclists’ attention is often compromised when interacting with notifications in traffic, hence increasing the likelihood of road accidents. To address this issue, we evaluate three notification interaction modalities and investigate their impact on the interaction performance while cycling: gaze-based Dwell Time, Gestures, and Manual And Gaze Input Cascaded (MAGIC) Pointing. In a user study (N=18), participants confirmed notifications in Augmented Reality (AR) using the three interaction modalities in a simulated biking scenario. We assessed the efficiency regarding reaction times, error rates, and perceived task load. Our results show significantly faster response times for MAGIC Pointing compared to Dwell Time and Gestures, while Dwell Time led to a significantly lower error rate compared to Gestures. Participants favored the MAGIC Pointing approach, supporting cyclists in AR selection tasks. Our research sets the boundaries for more comfortable and easier interaction with notifications and discusses implications for target selections in AR while cycling.