Researchers from North Carolina State University have developed an energy-efficient technique for accurately tracking a user’s physical activity based on data from wearable devices.
Though increasingly popular, one of the challenges with wearable health devices is trading off the accuracy of the measurements taken with the power needed for data analysis and storage.
The team developed a formula that allows the software on the device to identify meaningful activities and only record and store data that relates to these activities.
According to the group the chief challenge was finding a formula that allows the program to identify meaningful profiles (e.g., running, walking or sitting): if the formula is too general, the profiles are so broad as to be meaningless; and if the formula is too specific, you get so many activity profiles that it is difficult to store all of the relevant data.
To explore these challenges, the research team had graduate students come into a motion-capture lab and perform five different activities: golfing, biking, walking, waving and sitting.
The researchers then evaluated the resulting data using taus of zero seconds (i.e., one data point), two seconds, four seconds, and so on, all the way up to 40 seconds.
The researchers 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,” said Edgar Lobaton, the senior author of a paper on the group’s work.
According to Lobaton the team is now in the process of determining how well this approach would work using more real-world data.