A Dual-Accelerometer System for Classifying Physical Activity in Children and Adults. Issue 12 (December 2018)

• Record Type: Journal Article
• Authors: STEWART, TOM; NARAYANAN, ANANTHA; HEDAYATRAD, LEILA; NEVILLE, JONATHON; MACKAY, LISA; DUNCAN, SCOTT
• Is Part Of: Medicine and science in sports and exercise. Volume 50:Issue 12(2018)
• Publication Date: 2018-12
• Subjects: SEDENTARY BEHAVIOR -- MACHINE LEARNING -- VALIDATION -- TIME-USE EPIDEMIOLOGY; Sports medicine -- Periodicals; Exercise -- Physiological aspects -- Periodicals; Exercise -- Health aspects -- Periodicals; 612.044
• DOI: 10.1249/MSS.0000000000001717
• Languages: English
• ISSNs: 0195-9131

Abstract

ABSTRACT: Introduction: Accurately monitoring 24-h movement behaviors 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-h uninterrupted wear time compliance. This study extends this work by validating this system for classifying various physical activities and sedentary behaviors 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 modeling 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 nonambulatory 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. Conclusions: When previous wear time compliance results are taken together with our findings, it represents a promising step forward for monitoring and understanding 24-h time-use behaviors. The next step will be to examine the generalizability of these findings in a free-living setting. Abstract : Supplemental digital content is available in the text.