We introduce a multi-scale embossed map authoring tool (M-EMAT) that produces tactile maps of indoor environments from the building's structural layout and its 3D-scanned interiors on demand. Our tool renders indoor tactile maps at different spatial scales, representing a building's structure, a zoomed-in of a specific area, or an interior of a room. M-EMAT is very easy to use and produces accurate results even in the case of complex building layouts.

People with low vision often use screen magnification software. Screen magnification requires continuous control of the onscreen content by moving the focus of magnification with the mouse or the trackpad. In this contribution, we explore the possibility of controlling the focus of magnification by means of the user’s own eye gaze, which is measured by a commercial gaze tracker. We conducted two small experimental studies with individuals with impaired central vision, who used two screen magnification modalities to read two different types of documents. In the first study, mouse tracks and gaze point tracks were collected during manual control for later analysis. In the second study, the center of magnification was controlled by the user’s own gaze, using two different control mechanisms. This preliminary study highlights the potentials and shortcomings of gaze-contingent screen magnification control for easier access of onscreen content with low vision.

For people with poor upper limb mobility or control, interaction with a computer may be facilitated by adaptive and alternative interfaces. Visual head tracking has proven to be a viable pointing interface, which can be used when use of the mouse or trackpad is challenging. We are interested in new mechanisms to map the user's head motion to a pointer location on the screen. Towards this goal, we collected a data set of videos of participants as they were moving their head while following the motion of a marker on the screen. This data set could be used to training a machine learning system for pointing interface. We believe that by learning on real examples, this system may provide a more natural and satisfactory interface than current systems based on pre-defined algorithms.