In the visual condition no haptic feedback was provided, and the user's view of thump was limited to a one inch radius around the user's finger to more closely emulate the resolution of fingertip touch.

In the visual condition no haptic feedback was provided, and the user’s view of thump was limited to a one inch radius around the user’s finger to more closely emulate the resolution of fingertip touch.

In the non-visual condition, touching hallways, intersections or objects resulted in unique vibrations. Moving a finger over and object triggers an audio label with the object's name. Double tapping halls provided hall length information.

In the non-visual condition, touching hallways, intersections or objects resulted in unique vibrations. Moving a finger over and object triggers an audio label with the object’s name. Double tapping halls provided hall length information.

The Cognitive Map Transfer Study aims to explore how technology can help blind or visually impaired people navigate an indoor space. Various studies have shown that people are able to complete map recreation tasks from haptic maps, but there has been limited testing to determine if this learning procedure can assist in real-world navigation. This study aims to test the efficacy of navigation through spaces learned via haptic maps in comparison to visual maps.

This study was conducted using a visual and a non-visual map condition on an Android tablet. I created applications for each condition using Android Studio. Both conditions log the user’s finger position, current object touched (i.e. hall or object) at quarter second intervals. This will make provide the data needed to late make analysis about the learning time and finger scanning procedure used by participants.

I also contributed to the study procedure and script, as well as carefully selecting the testing routes. Two different routes were used for this study. They were balanced for number of decision points, route length and number of in-hall fire-doors. This study is currently being run. Data analysis will begin in January and the study will be written up early in 2017.