The Faculty of Engineering, University of Technology, Sydney, Australia.
Med Biol Eng Comput. 2009 Oct;47(10):1111-7. doi: 10.1007/s11517-009-0534-0.
The main motivation of this study is to establish an ambulatory cardio-respiratory analysis system for the monitoring and evaluation of exercise and regular daily physical activity. We explored the estimation of oxygen uptake by using noninvasive portable sensors. These sensors are easy to use but may suffer from malfunctions under free living environments. A promising solution is to combine sensors with different measuring mechanisms to improve both reliability and accuracy of the estimation results. For this purpose, we selected a wireless heart rate sensor and a tri-axial accelerometer to form a complementary sensor platform. We analyzed the relationship between oxygen uptake measured by gas analysis and data collected from the simple portable sensors using multivariable nonlinear modeling approaches. It was observed that the resulting nonlinear multivariable model could not only achieve a better estimate compared with single input single output models, but also had greater potential to improve reliability.
本研究的主要动机是建立一个可移动的心肺分析系统，用于监测和评估运动和日常体育活动。我们探索了使用非侵入性便携式传感器来估计耗氧量。这些传感器易于使用，但在自由生活环境下可能会出现故障。一个有前途的解决方案是将传感器与不同的测量机制相结合，以提高估计结果的可靠性和准确性。为此，我们选择了一个无线心率传感器和一个三轴加速度计来形成一个互补的传感器平台。我们使用多变量非线性建模方法分析了通过气体分析测量的耗氧量与从简单便携式传感器收集的数据之间的关系。结果表明，所得到的非线性多变量模型不仅可以与单输入单输出模型相比实现更好的估计，而且具有更大的提高可靠性的潜力。
