Researchers tested college students’ collision avoidance techniques.
There soon could be a smartphone app designed to prevent the text-induced face plant.
Juan-David Hincapié-Ramos, a postdoctoral researcher at the University of Manitoba’s human-computer interaction lab, is developing a smartphone app called CrashAlert, which uses a camera that senses depth and identifies oncoming objects — lampposts, for example — before a person runs into them.
The smartphone technology could be valuable — and a great way to avoid embarrassment — for college students with their heads down, reading and sending texts, as they make their way to their next class.
In a published paper, Hincapié-Ramos cited a 2008 report that showed a two-fold increase in “eyes-busy interaction- related accidents,” prompting some municipalities to ban the use of mobile devices while walking around local streets.
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“Unlike navigation aids for the visually-impaired which rely on audio or vibrotactile cues, CrashAlert displays a small slice of the depth camera’s image as a minimal footprint display on the mobile’s screen,” Hincapié-Ramos wrote. “With an extended field of view, users can take simpler and early corrective actions upon noticing a potential collision.”
The paper detailing technology behind the CrashAlert app was co-authored by a University of Manitoba associate professor, and will be presented in May at the Computer Human Interaction conference in Paris.
The researchers, in observing people using their mobile devices while walking, said these people relied almost entirely on their peripheral vision to avoid collisions with objects and other people. The most common “corrective action” used to avoid a collision was to raise the head after several checks of the surrounding area.
Using CrashAlert, a bright red square will appear on the person’s mobile device once the walker gets within two meters of the oncoming object.