If you drink, don’t drive’ , few slogans have remained as engraved as this one from a 1985 campaign by the General Directorate of Traffic ( DGT ). Stevie Wonder sang in the back seat of a convertible and, finally, the artist North American said the mythical phrase in Spanish.
As much as the slogan has passed into history, the Alcohol and drugs are to blame for more than 40% of deaths at the wheel , according to the DGT.
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Some of these deaths, perhaps, would have been avoided if a mobile phone had warned them that they were drunk . Many others do not, because, probably, those drivers did not need any device to alert them to the danger of driving in their state. They were absolutely aware of it and yet they pulled away.
Harsh awareness campaigns, raw images, apps , everything is welcome to tackle the scourge of deaths on the roads . Something similar must have been thought by Brian Suffoletto, now in the Department of Emergency Medicine at Stanford School of Medicine and formerly at the University of Pittsburgh School of Medicine.
When he was in this second academic center, he began, together with other researchers, a project that, in the future, would make the phones know if we are drunk .
The typical ‘try to walk in a straight line’, but with technology
Suffoletto says that a friend of his died in a traffic accident and that, in his experience as an emergency doctor, he saw many cases of people injured . This led him to start this experiment.
To do this, they recruited 23 adults aged 21 years or older, who consumed alcohol at least once a week. In a lab, they asked participants to walk 10 steps forward and 10 steps back with a smartphone placed on their lower back . The devices mobile measured acceleration and movement.
Stock image of a breathalyzer.
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Subsequently, they drank vodka to reach a concentration of 0.2 grams of alcohol per liter of blood and repeated the walk every hour for the next seven, while, thanks to mobile phones, the movements were still analyzed. With all the data, they say, they have drawn up a mathematical model that can detect the level of alcohol poisoning with more precision than the usual breathalyzers.