Transient and steady state estimation of human oxygen uptake based on noninvasive portable sensor measurements

• Published in: Medical & biological engineering & computing Vol. 47; no. 10; pp. 1111 - 1117
• Main Authors: Su, Steven W, Celler, Branko G, Savkin, Andrey V, Nguyen, Hung T, Cheng, Teddy M, Guo, Ying, Wang, Lu
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Berlin/Heidelberg : Springer-Verlag 01-10-2009; Springer-Verlag; Springer Nature B.V
• Subjects: Acceleration; Accelerometers; Adult; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Computer Applications; Engineering; Exercise; Exercise - physiology; Exercise Test - methods; Experiments; Fitness equipment; Heart rate; Human Physiology; Humans; Imaging; Male; Models, Biological; Monitoring, Ambulatory - instrumentation; Monitoring, Ambulatory - methods; Oxygen Consumption - physiology; Physical fitness; Physiology; Radiology; Reliability; Sensors; Studies; Support vector machines; Technical Note; Telemetry - instrumentation; Telemetry - methods; Walking; Young Adult; Nonlinear modeling; Multivariable model; Oxygen uptake; Support vector machine; Portable sensors
• ISSN: 0140-0118; 1741-0444
• 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.