Tracking Physical Activity via Wearables Data

01 December 2016

Researchers at North Carolina State University have developed an energy-efficient technique to track a user’s physical activity based on data from wearable devices.

“Tracking physical activity is important because it is a key component for placing other health data in context,” says Edgar Lobaton, assistant professor of electrical and computer engineering. “For example, a spike in heart rate is normal when exercising, but can be an indicator of health problems in other circumstances.”

: Motion-capture technology helped researchers develop a method to accurately track users' physical activity based on data from wearable devices. Image credit: NCSU: Motion-capture technology helped researchers develop a method to accurately track users' physical activity based on data from wearable devices. Image credit: NCSUDevising technology for monitoring physical activity involves addressing two challenges. First, the program needs to know how much data to process when assessing activity. For example, looking at all of the data collected over a 10-second increment (tau) takes twice as much computing power as evaluating all of the data over a five-second tau.

The second challenge is determining how best to store that information. One solution is to lump similar activity profiles together under one heading.

For example, certain data signatures may all be grouped together under “running,” while others may be lumped together as “walking.” The objective is to find a formula that allows the program to identify meaningful profiles (for example, running, walking, or sitting).

If the formula is too general, the profiles will be so broad as to be meaningless; if the formula is too specific, there will be so many activity profiles that it will be difficult to store all of the relevant data.

To explore these issues, the research team had graduate student,s perform five different activities in a motion-capture lab: golfing, biking, walking, waving and sitting. The researchers then evaluated the resulting data using taus of zero seconds (that is, one data point), two seconds, four seconds, and so on, all the way up to 40 seconds. They then experimented with different parameters for classifying activity data into specific profiles.

“Based on this specific set of experimental data, we found that we could accurately identify the five relevant activities using a tau of six seconds,” Lobaton says. This allowed activities to be identified and related data stored efficiently.

“This is a proof-of-concept study, and we’re in the process of determining how well this approach would work using more real-world data,” Lobaton says. “However, we’re optimistic that this approach will give us the best opportunity to track and record physical activity data in a practical way that provides meaningful information to users of wearable health-monitoring devices.”

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