Have you been manipulating your activity trackers to claim reward or incentives from you health care providers and insurance companies? This may not be possible from now on.
Researchers from the Rehabilitation Institute of Chicago (RIC) in the US have designed a new and interesting way to train smartphone trackers to spot the difference between fake and real activity.
"We have shown how to train systems to make sure data is authentic," said lead study author Sohrab Saeb, a postdoctoral fellow at the Northwestern University.
In the study, scientists showed smartphones rigorously trained on normal and deceptive activity can spot deceptive behaviour and generalise it across individuals. If the tracker learns how one person cheats, it will recognize the same shady behaviour in someone else. The new method detects, for example, when a cheater shakes the phone while lounging on the couch, so the tracker will think he's broken a sweat on a brisk walk.
While systems trained on normal activity data predicted true activity with 38 percent accuracy, training on the data gathered during the deceptive behaviour increased their accuracy to 84 percent, the researchers pointed out. As participants in the study varied their methods of cheating, the activity trackers were tested and retrained up to six times.
The study included 14 subjects, 23 to 38 years old, who used a variety of cheating strategies.
To fake walking when they were actually sitting on a chair, the participants shook the phone with their hands, swung their hands back and forth or slipped the phone into their pockets and moved their torso or legs to induce sensor readings similar to a real walk. They also tried to fake sitting while they were actually walking. The study was published in PLOS ONE.