Accurate measurement of various human movement behaviors is essential in developing 24-h movement profiles. A dual-accelerometer system recently showed promising results for accurately classifying a broad range of behaviors in a controlled laboratory environment. As a progressive step, the aim of this study is to validate the same dual-accelerometer system in semi free-living
conditions in children and adults. The efficacy of several placement sites (e.g., wrist, thigh, back) was evaluated for comparison.
Thirty participants (15 children) wore three Axivity AX3 accelerometers alongside an automated clip camera (clipped to the lapel) that recorded video of their free-living
environment (ground truth criterion measure of physical activity
). Participants were encouraged to complete a range of daily-living activities within a 2-h timeframe. A random forest machine-learning classifier was trained using features generated from the raw accelerometer data. Three different placement combinations were examined (thigh–back, thigh–wrist, back–wrist), and their performance was evaluated using leave-one-out cross-validation
for the child and adult samples separately.
Results Machine learning
models developed using the thigh–back accelerometer combination performed the best in distinguishing seven distinct activity classes with an overall accuracy of 95.6% in the adult sample, and eight activity classes with an overall accuracy of 92.0% in the child sample. There was a drop in accuracy (at least 11.0%) when other placement combinations were evaluated.
study demonstrated that a dual-accelerometer system previously validated in a laboratory setting also performs well in semi free-living
conditions. Although these results are promising and progressive, further work is needed to expand the scope of this measurement system to detect other components of behavior (e.g., activity intensity and sleep) that are related to health.