Liu Shaopeng, Gao Robert X, He Qingbo, Staudenmayer John, Freedson Patty
Electromechanical Systems Laboratory, University of Connecticut, Storrs, CT 06269, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:1266-9. doi: 10.1109/IEMBS.2009.5333890.
Estimation of ventilation volume from dimensional changes of the rib cage and abdomen is of interest to researchers interested in quantifying internal exposure to environmental pollutants in the atmosphere. In this paper, we present different statistical regression models for estimating ventilation volume during free-living activities. The movements of the rib cage and abdomen were measured by piezoelectric sensor belts. Multiple linear regression as the calibration method was applied. Five regression models with different combinations out of thirteen features were developed and the performance of these models was compared through experimental study of 11 subjects. The effect of training approaches - model trained for each subject and for all subjects, and the effect of time intervals for computing features were also investigated. The results indicate that Model 2, combining respiratory features and breathing frequency, with a longer time intervals will lead to a higher accuracy.
对于想要量化大气中环境污染物内部暴露情况的研究人员来说，通过胸腔和腹部尺寸变化来估算通气量是他们感兴趣的内容。在本文中，我们提出了不同的统计回归模型，用于估算自由活动期间的通气量。胸腔和腹部的运动通过压电传感器带进行测量。采用多元线性回归作为校准方法。从13个特征中开发了5种具有不同组合的回归模型，并通过对11名受试者的实验研究比较了这些模型的性能。还研究了训练方法（为每个受试者和所有受试者训练模型）的效果以及计算特征的时间间隔的影响。结果表明，结合呼吸特征和呼吸频率且时间间隔较长的模型2将具有更高的准确性。
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