New research from the Hasso Plattner Institute in Potsdam, Germany, aims to quell the frustration and strife that can come when multiple people use a single touch screen. The project, called Bootstrapper, uses cameras below a table to identify different users by their shoes. Each set of shoes is linked to an account that keeps track of a person’s actions and preferences.
Unlike other approaches to differentiating between users, Bootstrapper uses low-cost hardware and allows a person’s hands to freely interact with the surface. As an added benefit, a user’s preferences can be stored according to her shoes, so when she leaves the table, it’s easier to resume an activity when she returns.
Previous approaches to the problem have involved affixing sensors to chairs, or using cameras positioned above a table. One approach required users to wear a ring that emits infrared, which was then tracked by the touch-table’s cameras.
Patrick Baudisch, professor of computer science at the Hasso Plattner Institute, who developed the prototype system with graduate students Stephan Richter and Christian Holz, says shoes are ideal to track because they offer distinct features such as colors, seams, laces, logos, or stripes. They also typically maintain contact with the ground, unlike hands on a tabletop or bottoms in chairs, so they’re easier to track.
Baudisch stresses that Bootstrapper is not intended as a security feature. “People can always spoof the system by buying the same shoes as someone else,” he notes. The goal is to make collaboration easier and to log different people’s usage over many sessions. The researchers, for example, used it to summarize users’ achievements in a mathematics software program.
Bootstrapper collects video of shoes using cameras positioned below the surface of the table. Software extracts information about the texture of the shoe and links it with actions on the touch screen that correspond to hands and arms aligned with the shoes. With a small sample of 18 users and 18 different shoes, the researchers demonstrated that the system could recognize a user with 89 percent accuracy.
Smaller design teams can now prototype and deploy faster.