School of Sport and Recreation, Auckland University of Technology, Auckland, NEW ZEALAND.
Med Sci Sports Exerc. 2020 Jan;52(1):252-258. doi: 10.1249/MSS.0000000000002107.
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.
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.
This validation 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.
在开发 24 小时活动概况时，准确测量各种人体运动行为至关重要。最近的双加速计系统在准确分类广泛的行为方面显示出了有前途的结果，在受控的实验室环境中。作为一个渐进的步骤，本研究的目的是验证相同的双加速计系统在儿童和成人的半自由生活条件下的有效性。评估了几个放置位置（例如，手腕，大腿，背部）的效果进行比较。
30 名参与者（15 名儿童）佩戴了三个 Axivity AX3 加速度计和一个自动夹式相机（夹在翻领上），该相机记录了他们自由生活环境的视频（体力活动的地面真实标准测量）。鼓励参与者在 2 小时的时间内完成一系列日常活动。使用从原始加速度计数据生成的特征训练随机森林机器学习分类器。检查了三种不同的放置组合（大腿-背部，大腿-手腕，背部-手腕），并分别对儿童和成人样本使用留一交叉验证评估其性能。
使用大腿-背部加速度计组合开发的机器学习模型在区分七个不同的活动类别的表现最好，在成人样本中的总体准确率为 95.6%，在儿童样本中的总体准确率为 92.0%。当评估其他放置组合时，准确性会下降（至少 11.0%）。
这项验证研究表明，以前在实验室环境中验证过的双加速计系统在半自由生活条件下也能很好地工作。虽然这些结果很有希望和进步，但需要进一步的工作来扩展这个测量系统的范围，以检测与健康相关的其他行为成分（例如，活动强度和睡眠）。
