Individual versus Group Calibration of Machine Learning Models for Physical Activity Assessment Using Body-Worn Accelerometers

Purpose We sought to determine if individually calibrated machine learning models yielded higher accuracy than a group calibration approach for physical activity intensity assessment. Methods Participants (n = 48) wore accelerometers on the right hip and nondominant wrist while performing activities of daily living in a semistructured laboratory and/or free-living setting. Criterion measures of activity intensity (sedentary, light, moderate, vigorous) were determined using direct observation. Data were reintegrated into 30-s epochs, and eight random forest models were created to determine physical activity intensity by using all possible conditions of training data (individual vs group), protocol (laboratory vs free-living), and placement (hip vs wrist). A 2 × 2 × 2 repeated-measures analysis of variance was used to compare epoch-level accuracy statistics (% accuracy, kappa [κ]) of the models when used to determine activity intensity in an independent sample of free-living participants. Results Main effects were significant for the type of training data (group: accuracy = 80%, κ = 0.59; individual: accuracy = 74% [P = 0.02], κ = 0.50 [P = 0.01]) and protocol (free-living: accuracy = 81%, κ = 0.63; laboratory: accuracy = 74% [P = 0.04], κ = 0.47 [P
Source: Medicine and Science in Sports and Exercise - Category: Sports Medicine Tags: SPECIAL COMMUNICATIONS: Methodological Advances Source Type: research