A Dual-Accelerometer System for Classifying Physical Activity in Children and Adults

Introduction Accurately monitoring 24-hour movement behaviours is a vital step for progressing the time-use epidemiology field. Past accelerometer-based measurement protocols are either hindered by lack of wear time compliance, or the inability to accurately discern activities and postures. Recent work has indicated that skin-attached dual-accelerometers exhibit excellent 24-hour uninterrupted wear time compliance. This study extends this work by validating this system for classifying various physical activities and sedentary behaviours in children and adults. Methods Seventy-five participants (42 children) were equipped with two Axivity AX3 accelerometers; one attached to their thigh, and one to their lower back. Ten activity trials (e.g., sitting, standing, lying, walking, running) were performed while under direct observation in a lab setting. Various time- and frequency-domain features were computed from raw accelerometer data, which were then used to train a random forest machine learning classifier. Model performance was evaluated using leave-one-out cross-validation. The efficacy of the dual-sensor protocol (relative to single sensors) was evaluated by repeating the modelling process with each sensor individually. Results Machine learning models were able to differentiate between six distinct activity classes with exceptionally high accuracy in both adults (99.1%) and children (97.3%). When a single thigh or back accelerometer was used, there was a pronounced drop in accuracy for non-ambulatory activities (up to a 26.4% decline). When examining the features used for model training, those that took the orientation of both sensors into account concurrently were more important predictors. Conclusion When previous wear time compliance results are taken together with our findings, it represents a promising step forward for monitoring and understanding 24-hour time-use behaviours. The next step will be to examine the generalisability of these findings in a free-living setting. Corresponding author: Dr Tom Stewart, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand. Phone: +64 9 921 9999 ext 7855. E-mail: tom.stewart@aut.ac.nz No external funding was received for this study. Participant incentives were covered using internal department funds. The results of this study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The results of the present study do not constitute endorsement by ACSM. © 2018 American College of Sports Medicine

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