New Mobile Technology for Visually-impaired People

      Scientists are developing new adaptive mobile technology that could enable visually-impaired people to 'see' through their smartphone or tablet.

        Specialists in computer vision and machine learning based at the University of Lincoln, UK, funded by a Google Faculty Research Award, are aiming to embed a smart vision system in mobile devices to help people with sight problems navigate unfamiliar indoor environments.

        Based on preliminary work on assistive technologies done by the Lincoln Centre for Autonomous Systems, the team plans to use colour and depth sensor technology inside new smartphones and tablets to enable 3D mapping and localization, navigation and object recognition.

        The team will then develop the best interface to relay that to users - whether that is vibrations, sounds or the spoken word.

      "This project will build on our previous research to create an interface that can be used to help people with visual impairments," said Project lead Dr Nicola Bellotto, an expert on machine perception and human-centred robotics from Lincoln's School of Computer Science.

       "There are many visual aids already available, from guide dogs to cameras and wearable sensors. Typical problems with the latter are usability and acceptability.

      "If people were able to use technology embedded in devices such as smartphones, it would not require them to wear extra equipment which could make them feel self-conscious.

       "There are also existing smartphone apps that are able to, for example, recognize an object or speak text to describe places. But the sensors embedded in the device are still not fully exploited.

     "We aim to create a system with 'human-in-the-loop' that provides good localization relevant to visually impaired users and, most importantly, that understands how people observe and recognize particular features of their environment," said Bellotto.

       A key aspect of the system will be its capacity to adapt to individual users' experiences, modifying the guidance it provides as the machine 'learns' from its landscape and from the human interaction.

        So, as the user becomes more accustomed to the technology, the quicker and easier it would be to identify the environment.