Purpose: The primary aim of this pilot study was to assess how well the combination of global positioning system (GPS) and accelerometer data predicted different activity modes.
Methods: Ten adults (seven male, three female; 23-51 yr) simultaneously wore a GPS unit and accelerometer during bouts of walking, jogging/running, bicycling, inline skating, or driving an automobile. Discriminant function analysis was used to identify a parsimonious combination of variables derived from accelerometer counts and steps and GPS speed that best classified mode. A total of 29 bouts were used to develop this classification criterion. This criterion was validated using two datasets generated from the complete collection of minute-by-minute values from all bouts.
Results: Model development with "calibration" data showed that two accelerometer variables alone (median counts and steps) resulted in 26 of 29 bouts (90%) being correctly classified. Prediction of activity mode using counts and steps in a minute-by-minute "validation" dataset (N = 200) was 86.5%. Using three variables from the accelerometer and GPS (median counts, steps and speed) resulted in correct classification in 27 of 29 activity bouts in the "calibration" data (93%). In the "validation" dataset comprising 200 min, the combination of accelerometer counts and steps and GPS speed were able to correctly classify 91% of the observations. Walking and bicycling minutes were correctly classified most frequently (96%). In another "validation" dataset consisting of activity bouts, this combination of variables resulted in correct classification in 42 of 43 bouts (98%).
Conclusion: This pilot study provides evidence that the addition of GPS to accelerometer monitoring improves physical activity mode classification to a small degree. Larger studies among free-living individuals and with an expanded range of activities are needed to replicate the current findings and further determine the merits of using GPS with accelerometers for mode identification.