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 because activity trackers can be easily deceived, Northwestern Medicine and Northwestern Rehabilitation Institute of Chicago (RIC) 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,” said lead study author Sohrab Saeb, a postdoctoral fellow at the Center for Behavioral Intervention Technologies at Northwestern University Feinberg School of Medicine. “We’ve shown how to train systems to make sure data is authentic.”
The study was published in PLOS One in December.
Some insurance companies offer discounts to individuals who are more active, Saeb said. Health care providers may monitor patients to see if they are following a clinician’s advice to do or refrain from certain activities to improve the outcome of their treatment.