Romero-Ugalde Hector M, Garnotel M, Doron M, Jallon P, Charpentier G, Franc S, Huneker E, Simon C, Bonnet S
University Grenoble Alpes, F-38000 Grenoble, France. CEA, LETI, MINATEC Campus, F-38054 Grenoble, France.
Physiol Meas. 2017 Jul 28;38(8):1599-1615. doi: 10.1088/1361-6579/aa7cdf.
Activity energy expenditure (EE) plays an important role in healthcare, therefore, accurate EE measures are required. Currently available reference EE acquisition methods, such as doubly labeled water and indirect calorimetry, are complex, expensive, uncomfortable, and/or difficult to apply on real time. To overcome these drawbacks, the goal of this paper is to propose a model for computing EE in real time (minute-by-minute) from heart rate and accelerometer signals.
The proposed model, which consists of an original branched model, uses heart rate signals for computing EE on moderate to vigorous physical activities and a linear combination of heart rate and counts per minute for computing EE on light to moderate physical activities. Model parameters were estimated from a given data set composed of 53 subjects performing 25 different physical activities (light-, moderate- and vigorous-intensity), and validated using leave-one-subject-out. A different database (semi-controlled in-city circuit), was used in order to validate the versatility of the proposed model. Comparisons are done versus linear and nonlinear models, which are also used for computing EE from accelerometer and/or HR signals.
The proposed piecewise model leads to more accurate EE estimations ([Formula: see text], [Formula: see text] and [Formula: see text] J kg min and [Formula: see text], [Formula: see text], and [Formula: see text] J kg min on each validation database).
This original approach, which is more conformable and less expensive than the reference methods, allows accurate EE estimations, in real time (minute-by-minute), during a large variety of physical activities. Therefore, this model may be used on applications such as computing the time that a given subject spent on light-intensity physical activities and on moderate to vigorous physical activities (binary classification accuracy of 0.8155).
活动能量消耗（EE）在医疗保健中起着重要作用，因此需要准确的EE测量方法。目前可用的参考EE获取方法，如双标水法和间接量热法，复杂、昂贵、让人不适且/或难以实时应用。为克服这些缺点，本文的目标是提出一种根据心率和加速度计信号实时（逐分钟）计算EE的模型。
所提出的模型由一个原始分支模型组成，在中度至剧烈体力活动中使用心率信号计算EE，在轻度至中度体力活动中使用心率和每分钟计数的线性组合计算EE。模型参数是从由53名受试者进行25种不同体力活动（轻度、中度和剧烈强度）组成的给定数据集中估计出来的，并使用留一法进行验证。为了验证所提出模型的通用性，使用了一个不同的数据库（半控制的城市环路）。与线性和非线性模型进行了比较，这些模型也用于根据加速度计和/或心率信号计算EE。
所提出的分段模型能得出更准确的EE估计值（在每个验证数据库上分别为[公式：见原文]、[公式：见原文]和[公式：见原文]焦耳/千克·分钟以及[公式：见原文]、[公式：见原文]和[公式：见原文]焦耳/千克·分钟）。
这种原始方法比参考方法更合适且成本更低，可以在各种体力活动期间实时（逐分钟）准确估计EE。因此该模型可用于计算给定受试者在轻度体力活动以及中度至剧烈体力活动上花费的时间等应用（二元分类准确率为0.8155）。
