Health care providers and insurance companies are increasingly relying on smartphone and wearable activity trackers to reward active individuals for healthy behavior or to monitor patients.
But trackers can be easily deceived.
Now, researchers have designed a way to train smartphone trackers to spot the difference between fake and real activity. 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 behavior increased their accuracy to 84 percent.
“As health care providers and insurance companies rely more on activity trackers, there is an imminent need to make these systems smarter against deceptive behavior,” says lead study author Sohrab Saeb, a postdoctoral fellow at Northwestern University Feinberg School of Medicine. “We’ve shown how to train systems to make sure data is authentic.”
