Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample
Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample
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DOI：
10.1152/japplphysiol.00309.2011
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发表时间：
2011-12-01
影响因子：
3.3
通讯作者：
Staudenmayer, John
中科院分区：
文献类型：
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作者：
Freedson, Patty S.;Lyden, Kate;Staudenmayer, John
[2]Freedson PS，Lyden K，Kozey-Keadle S，Staudenmayer J.从加速度计数据预测METS和活动类型的人工神经网络算法的评估：在独立样本上的验证。应用生理学杂志111：1804-1812,2011。2011年9月1日首次出版；DOI：10.1152/japplPhysiol.00309.2011。-我们实验室以前的工作为使用人工神经网络(NET)估计代谢当量(MET)并从加速计数据中识别活动类型提供了“概念证明”(Staudenmayer J，Pober D，Crouter S，Bassett D，Freedson P，J Appl Physiol107：1330-1307,2009年)。这项研究的目的是基于更大、更多样化的训练数据集开发新的网络，并将这些网络预测模型应用于一个独立的样本，以评估这种机器学习建模技术的健壮性和灵活性。NNet培训数据集(马萨诸塞大学)包括277名参与者，他们每人完成了11项活动。独立验证样本(n=65)(田纳西大学)完成了三项活动中的一项。标准措施是1)使用开路间接量热法评估测量的蛋氨酸；以及2)观察活动以确定活动类型。NNet输入变量包括五个加速度计计数分布特征和滞后-1自相关。在马萨诸塞大学训练和田纳西大学应用的NNet Met的偏差和均方根误差分别为+0.32和1.90 Mets。77%的活动被正确归类为久坐/轻度、中等强度或剧烈强度。在活动类型上，家庭活动和移动活动的正确分类正确率分别为98.1%和89.5%，体育活动的正确率为23.7%。当应用于独立样本时，这种机器学习技术的使用运行得相当好。我们建议创建一个开放获取的活动词典，包括来自广泛活动的加速计数据，从而进一步提高对大都会运动试验、活动强度和活动类型的预测精度。
Freedson PS, Lyden K, Kozey-Keadle S, Staudenmayer J. Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample. J Appl Physiol 111: 1804-1812, 2011. First published September 1, 2011; doi:10.1152/japplphysiol.00309.2011.-Previous work from our laboratory provided a "proof of concept" for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330-1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample (n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were + 0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type.