CARL: a running recognition algorithm for free-living accelerometer data

Abstract

Abstract Wearable accelerometers hold great promise for physical activity epidemiology and sports biomechanics. However, identifying and extracting data from specific physical activities, such as running, remains challenging. Objective. To develop and validate an algorithm to identify bouts of running in raw, free-living accelerometer data from devices worn at the wrist or torso (waist, hip, chest). Approach. The CARL (continuous amplitude running logistic) classifier identifies acceleration data with amplitude and frequency characteristics consistent with running. The CARL classifier was trained on data from 31 adults wearing accelerometers on the waist and wrist, then validated on free-living data from 30 new, unseen subjects plus 166 subjects from previously-published datasets using different devices, wear locations, and sample frequencies. Main results. On free-living data, the CARL classifier achieved mean accuracy (F 1 score) of 0.984 (95% confidence interval 0.962–0.996) for data from the waist and 0.994 (95% CI 0.991–0.996) for data from the wrist. In previously-published datasets, the CARL classifier identified running with mean accuracy (F 1 score) of 0.861 (95% CI 0.836–0.884) for data from the chest, 0.911 (95% CI 0.884–0.937) for data from the hip, 0.916 (95% CI 0.877–0.948) for data from the waist, and 0.870 (95% CI 0.834–0.903) for data from the wrist. Misclassification primarily occurred during activities with similar torso acceleration profiles to running, such as rope jumping and elliptical machine use. Significance. The CARL classifier can accurately identify bouts of running as short as three seconds in free-living accelerometry data. An open-source implementation of the CARL classifier is available at github.com/johnjdavisiv/carl.