Kolus Ahmet, Imbeau Daniel, Dubé Philippe-Antoine, Dubeau Denise
Department of Mathematics and Industrial Engineering, Polytechnique Montréal, Montréal, Canada.
Department of Mathematics and Industrial Engineering, Polytechnique Montréal, Montréal, Canada.
Appl Ergon. 2015 Sep;50:68-78. doi: 10.1016/j.apergo.2015.03.001. Epub 2015 Mar 25.
This paper presents a new model based on adaptive neuro-fuzzy inference systems (ANFIS) to predict oxygen consumption (V˙O2) from easily measured variables. The ANFIS prediction model consists of three ANFIS modules for estimating the Flex-HR parameters. Each module was developed based on clustering a training set of data samples relevant to that module and then the ANFIS prediction model was tested against a validation data set. Fifty-eight participants performed the Meyer and Flenghi step-test, during which heart rate (HR) and V˙O2 were measured. Results indicated no significant difference between observed and estimated Flex-HR parameters and between measured and estimated V˙O2 in the overall HR range, and separately in different HR ranges. The ANFIS prediction model (MAE = 3 ml kg(-1) min(-1)) demonstrated better performance than Rennie et al.'s (MAE = 7 ml kg(-1) min(-1)) and Keytel et al.'s (MAE = 6 ml kg(-1) min(-1)) models, and comparable performance with the standard Flex-HR method (MAE = 2.3 ml kg(-1) min(-1)) throughout the HR range. The ANFIS model thus provides practitioners with a practical, cost- and time-efficient method for V˙O2 estimation without the need for individual calibration.
本文提出了一种基于自适应神经模糊推理系统（ANFIS）的新模型，用于根据易于测量的变量预测耗氧量（V˙O2）。ANFIS预测模型由三个用于估计Flex-HR参数的ANFIS模块组成。每个模块都是基于对与该模块相关的数据样本训练集进行聚类而开发的，然后针对验证数据集对ANFIS预测模型进行测试。58名参与者进行了迈耶（Meyer）和弗伦吉（Flenghi）阶梯试验，在此期间测量了心率（HR）和V˙O2。结果表明，在整个心率范围内以及在不同心率范围内分别观察到的和估计的Flex-HR参数之间，以及测量的和估计的V˙O2之间没有显著差异。ANFIS预测模型（平均绝对误差MAE = 3 ml·kg⁻¹·min⁻¹）表现出比雷尼（Rennie）等人的模型（MAE = 7 ml·kg⁻¹·min⁻¹）和凯特尔（Keytel）等人的模型（MAE = 6 ml·kg⁻¹·min⁻¹）更好的性能，并且在整个心率范围内与标准Flex-HR方法（MAE = 2.3 ml·kg⁻¹·min⁻¹）具有相当的性能。因此，ANFIS模型为从业者提供了一种实用、经济高效且省时的V˙O2估计方法，无需进行个体校准。
