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Smarter activity tracker knows when you’re just pretending to work out

by • January 13, 2016 • No Comments

Tricking your fitness tracker into logging a workout when you are in fact simply just laying on the couch appears like a fairly futile exercise, but there’s additional to the equation than simply just fooling by yourself. Incertainrs and health care providers are increasingly relying on tracking data to offer incentives, reduced premiums and keep tabs on clients behavior. This is cause for concern for one team of US researchers, which has developed an activity tracking smartphone app which can advantageous distinguish between real and imitated physical movement.

Fitness trackers such as Fitbit and Jawbone which monitor things like heart rate and the amount of steps taken have become a useful tool for health incertainrs looking for a competitive advantage. One example is New York’s Oscar Health – last year the company decided to ship customers complimentary fitness trackers and pledged to reward those clocking up adequate steps with Amazon gift cards.

Australian company MLC offers participants 10 percent discounts on life insurance if they meet certain physical criteria, as monitored through a Basis Peak fitness tracker. And these connected devices are making it simpler than at any time to share fitness data with doctors, a situation where misinformation may pose even additional serious problems.

“As health care providers and insurance companies rely additional on activity trackers, there is an imminent require to create these systems smarter against deceptive behavior,” says Sohrab Saeb, a postdoctoral man at Northwestern University Feinberg School of Medicine. “We’ve shown how to train systems to create certain data is authentic.”

Saeb led a team of researchers to develop a fitness tracking system which can advantageous detect when the subject is cheating. This began with 14 subjects, who were asked to try and trick an Android smartphone activity app into logging physical tasks they weren’t in fact carrying out, such as shaking the phone while seated, or swinging their arms back and forth to mimic walking.

If they succeeded, the team utilized motion data captured of the device’s accelerometer and gyroscope to retrain the app to recognize their trickery. This system was repeated up to six times to account for the varied methods of cheating, until the subjects were unable to deceive the system.

The team says regular activity classifiers predict true activity with around 38 percent accuracy, while their solution based on data gleaned of the cheating exercises resulted in 84 percent accuracy. It claims which learning the cheating tactics of one man helps to detect the tactics of others, and the technology can therefore be generalized to create for additional effective activity classification overall.

“Very few studies have tried to create activity tracking recognition robust against cheating,” says senior author Konrad Kording, a scientist at Rehabilitation Institute of Chicago (RIC) and member of the research team. “This technology may have broad implications for companies which create activity trackers and insurance companies alike as they seek to additional reliably record movement.”

While smartphones were utilized in this study, the researchers say the same technology can be applied to fitness tracking bracelets and other wearables as well.

The research was published in the journal PLOS One.

Source: Northwestern University


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