Energy Expenditure Prediction Using Raw Accelerometer Data in Simulated Free Living
Energy Expenditure Prediction Using Raw Accelerometer Data in Simulated Free Living
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DOI：
10.1249/mss.0000000000000597
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发表时间：
2015-08-01
期刊：
影响因子：
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通讯作者：
Pfeiffer, Karin A.
中科院分区：
文献类型：
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作者：
Montoye, Alexander H. K.;Mudd, Lanay M.;Pfeiffer, Karin A.
本研究的目的是开发、验证和比较放置在臀部、大腿和手腕上的加速度计的能量消耗（EE）预测模型，使用简单的加速度计特征作为EE预测模型的输入变量。方法44名健康成人参加了一项90分钟的半结构化模拟自由生活活动方案。在研究过程中，参与者进行了14种不同的久坐、走动、生活方式和锻炼活动，每次3-10分钟。参与者选择活动的顺序、持续时间和强度。佩戴四个加速度计（右臀部，右大腿，以及左右手腕）来预测EE，并与标准测量（便携式代谢分析仪）测量的结果进行比较。使用留一交叉验证方法，创建人工神经网络（ann）来预测每个加速度计的EE。使用Pearson相关性、均方根误差和偏倚来评估人工神经网络的准确性。使用不同的输入特征开发了几个人工神经网络，以确定最相关的模型使用。结果4种加速度计的神经网络测量精度较高，预测EE的相关系数为0.80。大腿加速度计提供了最高的总体精度（r = 0.90）和最低的均方根误差（1.04 METs），并且当在预测模型中使用较少的输入变量时，大腿加速度计与其他监测器之间的差异更加明显。所有预测模型对情感表达的预测都没有总体偏差。结论单个加速度计放置在大腿上提供了最高的预测精度，尽管佩戴在手腕或臀部的监测器也可以提供较高的测量精度。
PurposeThe purpose of this study was to develop, validate, and compare energy expenditure (EE) prediction models for accelerometers placed on the hip, thigh, and wrists using simple accelerometer features as input variables in EE prediction models.MethodsForty-four healthy adults participated in a 90-min, semistructured, simulated free-living activity protocol. During the protocol, participants engaged in 14 different sedentary, ambulatory, lifestyle, and exercise activities for 3-10 min each. Participants chose the order, duration, and intensity of activities. Four accelerometers were worn (right hip, right thigh, as well as right and left wrists) to predict EE compared with that measured by the criterion measure (portable metabolic analyzer). Artificial neural networks (ANNs) were created to predict EE from each accelerometer using a leave-one-out cross-validation approach. Accuracy of the ANN was evaluated using Pearson correlations, root mean square error, and bias. Several ANNs were developed using different input features to determine those most relevant for use in the models.ResultsThe ANNs for all four accelerometers achieved high measurement accuracy, with correlations of r > 0.80 for predicting EE. The thigh accelerometer provided the highest overall accuracy (r = 0.90) and lowest root mean square error (1.04 METs), and the differences between the thigh and the other monitors were more pronounced when fewer input variables were used in the predictive models. None of the predictive models had an overall bias for prediction of EE.ConclusionsA single accelerometer placed on the thigh provided the highest accuracy for EE prediction, although monitors worn on the wrists or hip can also be used with high measurement accuracy.