School of Sport and Recreation, Auckland University of Technology, Auckland, NEW ZEALAND.
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, NEW ZEALAND.
Med Sci Sports Exerc. 2018 Dec;50(12):2595-2602. doi: 10.1249/MSS.0000000000001717.
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.
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.
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.
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.
准确监测 24 小时运动行为是推进时间使用流行病学领域的重要步骤。过去基于加速度计的测量方案要么由于缺乏佩戴时间依从性而受到阻碍，要么无法准确区分活动和姿势。最近的工作表明，皮肤附着的双加速度计表现出出色的 24 小时不间断佩戴时间依从性。本研究通过验证该系统在分类儿童和成人各种身体活动和久坐行为方面的性能，扩展了这项工作。
75 名参与者（42 名儿童）配备了两个 Axivity AX3 加速度计；一个安装在大腿上，一个安装在背部。在实验室环境中直接观察下进行了十项活动试验（例如，坐、站、躺、走、跑）。从原始加速度计数据中计算了各种时间和频率域特征，然后将其用于训练随机森林机器学习分类器。使用留一交叉验证评估模型性能。通过单独使用每个传感器重复建模过程，评估了双传感器协议（相对于单个传感器）的效果。
机器学习模型能够以极高的准确度区分六种不同的活动类别，在成人（99.1%）和儿童（97.3%）中均表现出色。当使用单个大腿或背部加速度计时，非步行活动的准确性明显下降（高达 26.4%的下降）。当检查用于模型训练的特征时，同时考虑两个传感器方向的特征是更重要的预测因素。
当结合以前的佩戴时间依从性结果来看待我们的发现时，这代表了监测和理解 24 小时时间使用行为的一个有前途的进展。下一步将在自由生活环境中检验这些发现的通用性。
