title: A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers.
abstract: Wrist accelerometers are being used in population level surveillance of physical activity (PA) but more research is needed to evaluate their validity for correctly classifying types of PA behavior and predicting energy expenditure (EE). In this study we compare accelerometers worn on the wrist and hip, and the added value of heart rate (HR) data, for predicting PA type and EE using machine learning. Forty adults performed locomotion and household activities in a lab setting while wearing three ActiGraph GT3X+ accelerometers (left hip, right hip, non-dominant wrist) and a HR monitor (Polar RS400). Participants also wore a portable indirect calorimeter (COSMED K4b2), from which EE and metabolic equivalents (METs) were computed for each minute. We developed two predictive models: a random forest classifier to predict activity type and a random forest of regression trees to estimate METs. Predictions were evaluated using leave-one-user-out cross-validation. The hip accelerometer obtained an average accuracy of 92.3% in predicting four activity types (household, stairs, walking, running), while the wrist accelerometer obtained an average accuracy of 87.5%. Across all 8 activities combined (laundry, window washing, dusting, dishes, sweeping, stairs, walking, running), the hip and wrist accelerometers obtained average accuracies of 70.2% and 80.2% respectively. Predicting METs using the hip or wrist devices alone obtained root mean square errors (rMSE) of 1.09 and 1.00 METs per 6 min bout, respectively. Including HR data improved MET estimation, but did not significantly improve activity type classification. These results demonstrate the validity of random forest classification and regression forests for PA type and MET prediction using accelerometers. The wrist accelerometer proved more useful in predicting activities with significant arm movement, while the hip accelerometer was superior for predicting locomotion and estimating EE.
authors.affiliation: Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0407, USA.
authors.forename: Katherine
authors.initials: K
authors.lastname: Ellis
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Kerr
authors.forename: Suneeta
authors.initials: S
authors.lastname: Godbole
authors.forename: Gert
authors.initials: G
authors.lastname: Lanckriet
authors.forename: David
authors.initials: D
authors.lastname: Wing
authors.forename: Simon
authors.initials: S
authors.lastname: Marshall
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D000328:Adult; D001185:Artificial Intelligence; D002153:Calorimetry, Indirect; D004734:Energy Metabolism; D005260:Female; D006339:Heart Rate; D006615:Hip; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D009043:Motor Activity / Q000502:physiology; D014953:Wrist