Estimating Energy Expenditure with ActiGraph GT9X Inertial Measurement Unit
Estimating Energy Expenditure with ActiGraph GT9X Inertial Measurement Unit
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DOI：
10.1249/mss.0000000000001532
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发表时间：
2018-05-01
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影响因子：
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通讯作者：
Crouter, Scott E.
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文献类型：
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作者：
Hibbing, Paul R.;Lamunion, Samuel R.;Crouter, Scott E.
目的本研究的目的是探讨从ActiGraph GT 9 X陀螺仪和磁力计数据是否改善基于加速度计的能量消耗（EE）的预测。方法30名参与者（平均SD：年龄，23.0 ± 2.3岁;体重指数，25.2 +/- 3.9 kgm（-2））自愿完成研究。参与者佩戴五个GT 9 X显示器（右髋、两个手腕和两个脚踝），同时进行从休息到跑步的10项活动。在试验期间佩戴Cosmed K4 b（2），作为EE（30秒平均值）的标准测量值，以MET表示。使用欧几里德范数减一（ENMO; 1-s历元）将三轴加速度计数据（80 Hz）转换为毫G。陀螺仪数据（100 Hz）表示为矢量幅度（GVM），单位为度每秒（1-s历元），磁力计数据（100 Hz）表示为每5 s的方向变化。每个活动的4-6分钟用于分析。针对每个磨损位置开发了三种二次回归算法：1）ENMO，2）ENMO和GVM，以及3）ENMO、GVM和方向变化。结果在仅加速度计的算法中加入陀螺仪后，RMSE降低0.0 MET（右手腕）~ 0.17 MET（右脚踝），MAPE降低0.1%（右手腕）~ 6.0%（髋关节）。当增加方向变化时，RMSE变化0.03METs，MAPE变化0.21%。结论与仅使用加速度计相比，在髋关节和踝关节联合使用陀螺仪和加速度计可提高EE的个体水平预测。对于手腕，添加陀螺仪产生的变化可以忽略不计。磁力计没有有意义地改善任何算法的估计。
Purpose The purpose of this study was to explore whether gyroscope and magnetometer data from the ActiGraph GT9X improved accelerometer-based predictions of energy expenditure (EE).Methods Thirty participants (mean SD: age, 23.0 2.3 yr; body mass index, 25.2 +/- 3.9 kgm(-2)) volunteered to complete the study. Participants wore five GT9X monitors (right hip, both wrists, and both ankles) while performing 10 activities ranging from rest to running. A Cosmed K4b(2) was worn during the trial, as a criterion measure of EE (30-s averages) expressed in METs. Triaxial accelerometer data (80 Hz) were converted to milli-G using Euclidean norm minus one (ENMO; 1-s epochs). Gyroscope data (100 Hz) were expressed as a vector magnitude (GVM) in degrees per second (1-s epochs) and magnetometer data (100 Hz) were expressed as direction changes per 5 s. Minutes 4-6 of each activity were used for analysis. Three two-regression algorithms were developed for each wear location: 1) ENMO, 2) ENMO and GVM, and 3) ENMO, GVM, and direction changes. Leave-one-participant-out cross-validation was used to evaluate the root mean square error (RMSE) and mean absolute percent error (MAPE) of each algorithm.Results Adding gyroscope to accelerometer-only algorithms resulted in RMSE reductions between 0.0 METs (right wrist) and 0.17 METs (right ankle), and MAPE reductions between 0.1% (right wrist) and 6.0% (hip). When direction changes were added, RMSE changed by 0.03 METs and MAPE by 0.21%.Conclusions The combined use of gyroscope and accelerometer at the hip and ankles improved individual-level prediction of EE compared with accelerometer only. For the wrists, adding gyroscope produced negligible changes. The magnetometer did not meaningfully improve estimates for any algorithms.