ASSIST: Evaluating the usability and performance of an indoor navigation assistant for blind and visually impaired people

ABSTRACT

This paper describes the interface and testing of an indoor navigation app – ASSIST – that guides blind & visually impaired (BVI) individuals through an indoor environment with high accuracy while augmenting their understanding of the surrounding environment. ASSIST features personalized interfaces by considering the unique experiences that BVI individuals have in indoor wayfinding and offers multiple levels of multimodal feedback. After an overview of the technical approach and implementation of the first prototype of the ASSIST system, the results of two pilot studies performed with BVI individuals are presented – a performance study to collect data on mobility (walking speed, collisions, and navigation errors) while using the app, and a usability study to collect user evaluation data on the perceived helpfulness, safety, ease-of-use, and overall experience while using the app. Our studies show that ASSIST is useful in providing users with navigational guidance, improving their efficiency and (more significantly) their safety and accuracy in wayfinding indoors. Findings and user feedback from the studies confirm some of the previous results, while also providing some new insights into the creation of such an app, including the use of customized user interfaces and expanding the types of information provided.

KEYWORDS:

• blind and visually impaired
• human subject studies
• Indoor navigation
• mobile apps

Acknowledgments

This research was supported by the U.S. Department of Homeland Security (DHS), administered by the Oak Ridge Institute for Science and Education (ORISE) under DOE contract #DE-AC05- 06OR23100 and #DE-SC0014664. This work is also supported by the U.S. National Science Foundation (NSF) through Awards #EFRI-1137172, #CBET-1160046, #CNS-1737533 and #IIP-1827505; the VentureWell (Award #10087-12); a Bentley-CUNY Collaborative Research Agreement 2017-2020; the U.S. Office of the Director of National Intelligence (ODNI) via Intelligence Community Center for Academic Excellence (IC CAE) at Rutgers (Awards #HHM402-19-1-0003 and #HHM402-18-1-0007); and NYSID via the CREATE Program. Special thanks go to all of our subjects for their participation and cooperation as well as for providing extremely helpful feedback in improving our system. Readers may view a demo video showing ASSIST’s system components and human subjects testing through this link: https://youtu.be/Hq1EYS9Jncg

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website.

Additional information

Funding

This work was supported by the VentureWell [#10087-12]; NYSID [CREATE 2017-2018]; Bentley Systems, Inc. [Bentley-CUNY Collaborative Research Agreement 2017]; U.S. National Science Foundation [#CBET-1160046,#CNS-1737533,#EFRI-1137172,#IIP-1827505]; U.S. Department of Homeland Security [#DE-AC05- 06OR23100,#DE-SC0014664]; the U.S. Office of the Director of National Intelligence via IC CAE at Rutgers [Awards #HHM402-19-1-0003 and #HHM402-18-1-0007].