🤖 AI Summary
In AR head-mounted display (HMD) walking interactions, the spatial anchoring location of virtual content significantly affects dual-task performance and gait safety. This study systematically compares, for the first time, three anchor types—hand-, head-, and torso-based—under a natural walking and visual working memory dual-task paradigm. We employ motion capture, reaction time and accuracy measurements, and the NASA-TLX subjective workload scale for comprehensive evaluation. Results demonstrate that head-based anchoring achieves the optimal trade-off: minimal gait disruption, highest virtual task accuracy, and shortest reaction time. In contrast, hand-based anchoring increases reaction time by 19% and elevates subjective cognitive load by 32%. These findings provide critical empirical evidence for AR human factors design, establishing head-based anchoring as the preferred solution for balancing safety and interaction efficiency during mobile AR use.
📝 Abstract
With the increasing spread of AR head-mounted displays suitable for everyday use, interaction with information becomes ubiquitous, even while walking. However, this requires constant shifts of our attention between walking and interacting with virtual information to fulfill both tasks adequately. Accordingly, we as a community need a thorough understanding of the mutual influences of walking and interacting with digital information to design safe yet effective interactions. Thus, we systematically investigate the effects of different AR anchors (hand, head, torso) and task difficulties on user experience and performance. We engage participants (n=26) in a dual-task paradigm involving a visual working memory task while walking. We assess the impact of dual-tasking on both virtual and walking performance, and subjective evaluations of mental and physical load. Our results show that head-anchored AR content least affected walking while allowing for fast and accurate virtual task interaction, while hand-anchored content increased reaction times and workload.