Energy-Efficient Method for Tracking Physical Activity with Wearable Devices

Motion capture technology helped researchers at NC State University develop an energy-efficient technique for accurately tracking a user's physical activity based on data from wearable devices. (CREDIT: North Carolina State University)

A team of researchers from North Carolina State University have developed an energy-efficient method to accurately track the physical activity of a user based on data derived wearable devices.

One objective for wearable health technologies is to detect and track physical activity of the wearer. However, achieving this goal requires a trade-off between accuracy and the power required for data analysis and storage, which is an issue, given the minimal power available for wearable devices.

Tracking physical activity is important because it is a key component for placing other health data in context. For example, a spike in heart rate is normal when exercising, but can be an indicator of health problems in other circumstances.

Edgar Lobaton, Assistant Professor, NC State

Engineering technology for tracking physical activity involves finding solutions for two challenges. First, the program has to know the amount of data that should be processed while assessing activity. For instance, assessing all of the data gathered over a 10-second increment, or tau, takes twice as much computing power as assessing all of the data over a five-second tau.

The second challenge is to find a way to store that data. One solution to this is to categorize similar activity profiles together under a single heading. For instance, particular data signatures could all be categorized together under “running,” while others could be grouped together as “walking.”

The challenge now is to find a formula that enables the program to detect meaningful profiles (e.g., walking, running, or sitting): if the formula is too universal, then the profiles are so broad they become meaningless; and if the formula is too definite, a number of activity profiles will be generated that it is difficult to store all of the applicable data.

To investigate these challenges, the research team asked a few graduate students to perform five different activities: biking, walking, golfing, sitting, and waving in a motion-capture lab.

The researchers then analyzed the resulting data using taus of zero seconds (i.e., one data point), two seconds, four seconds, and so on, right up to 40 seconds. The researchers then experimented with varying parameters for categorizing 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. This means we could identify activities and store related data 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. 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.

Edgar Lobaton, Assistant Professor, NC State

The research paper, “Hierarchical Activity Clustering Analysis for Robust Graphical Structure Recovery,” will be presented at the 2016 IEEE Global Conference on Signal and Information Processing, being held in Washington, D.C. between December 7 and 9. Lead author of the paper is Namita Lokare, a Ph.D. student at NC State. The co-authors are Daniel Benavides and Sahil Juneja, of NC State.

The research was conducted with support from the National Science Foundation’s Nanosystems Engineering Research Center for Advanced Self-Powered Systems of Integrated Sensors and Technologies (ASSIST) under grant EEC-1160483. The aim of the ASSIST Center, which is located at NC State, is to make wearable technologies, which are powered by a user’s body heat or movement, practical for long-term monitoring of health.

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