AR in the library: A pilot study of multi-target acquisition usability

Libraries use call numbers to organize their books and enable patrons to locate them. To keep the books in order, library workers conduct a time-consuming and tedious task called “shelf-reading.” Workers look at the call numbers on the spines of each book in the library, one at a time, to make sure they are in the correct places. ShelvAR is an augmented reality shelf-reading system for smart phones that reduces time spent, increases accuracy, and produces an inventory of the books on their shelves as a byproduct. Shelf-reading requires rapid acquisition of many targets (books). Unlike many target acquisition tasks considered in the AR literature, the user is not trying to select a single target from among many. Instead, the user is trying to scan all of the targets, and must be able to easily double-check that none were missed. Our goal is to explore the usability of augmented reality applications for this type of “multiple target acquisition” task. We present the results of a pilot study on the effectiveness of ShelvAR. We demonstrate that individuals with no library experience are just as fast and accurate, when using ShelvAR, as experienced library workers at the shelf-reading task.