Citation

Pundlik S, Shivshanker P, Traut-Savino T, Luo G. Transl. Vis. Sci. Technol. 2024; 13(1): e11.

Copyright

(Copyright © 2024, Association for Research in Vision and Ophthalmology)

DOI

10.1167/tvst.13.1.11

PMID

38224330

Abstract

PURPOSE: GPS location-based navigation apps are insufficient to aid blind and visually impaired (BVI) travelers for micro-navigation tasks, such as finding the exact location of bus stops. The resulting large gaps could lead to BVI travelers missing their bus. We evaluated the ability of a signage detection mobile app, All_Aboard, to guide BVI travelers precisely to the bus stops compared to Google Maps alone.

METHODS: The All_Aboard app detected bus stop signs in real-time via smartphone camera using a deep neural network model, and provided distance coded audio feedback to help localize the detected sign. BVI individuals used the All_Aboard and Google Maps app to localize 10 bus stops each in downtown and suburban Boston, Massachusetts. For each bus stop, the subjects used both apps to navigate as close as possible to the physical bus stop sign, starting from 30 to 50 meters away. The outcome measures were success rate and gap distance between the app-indicated location and the actual physical location of the bus stop.

RESULTS: The study involved 24 legally blind participants (mean age [SD] = 51 [14] years; 11 [46%] women). The success rate of the All_Aboard app (91%) was significantly higher than the Google Maps (52%, P < 0.001). The gap distance when using the All_Aboard app was significantly lower (mean = 1.8, 95% confidence interval [CI] = 1.2-2.3 meters) compared to the Google Maps alone (mean = 7, 95% CI = 6.5-7.5 meters, P < 0.001).

CONCLUSIONS: All_Aboard micro-navigation app guided BVI travelers to bus stops more accurately and reliably than a location-based macro-navigation app alone. TRANSLATIONAL RELEVANCE: The All_Aboard app together with a macro-navigation app can potentially help BVI individuals independently access public transportation.

Language: en

Keywords

Adolescent; Humans; Female; Male; Blindness; *Mobile Applications; Neural Networks, Computer