Detecting motor vehicle travel in accelerometer data

Chronic Obstructive Pulmonary Disease (COPD) frequently has a significant impact on patients' everyday activity. Because of this, accurate measurement of daily activity is of particular interest. Although accelerometers are an objective means of measuring daily activity, these devices sense vibrations and erroneously score motor vehicle travel (MVT) as moderate physical activity. It is the objective of this study to develop a new method to analyze accelerometry data that would accurately classify MVT as non-acceleration, or sitting/standing. As sitting/standing has a different pattern of count-to-count variability than walking, we hypothesized that a rolling standard deviation (RSD), which is a measurement of volatility in the data, would more accurately classify periods of MVT than analysis based on activity counts alone. Twenty-two subjects with COPD were studied. A training set of 15% of the dataset was used to establish an RSD-threshold during MVT based on the upper 95%-confidence interval. The accuracy of the RSD thresholds were tested and presented as sensitivity, specificity and receiver operating curves. Results demonstrated high sensitivity and specificity suggesting that the RSD not only accurately classified MVT, but had a low rate of misclassification. The RSD analysis scored more MVT as sitting/standing than assessment by VMU alone. The accuracy of accelerometers to define the profile of daily activity in sedentary populations, such as those with COPD, is greatly improved.