Dynamic modelling of heart rate response under different exercise intensity.

Heart rate is one of the major indications of human cardiovascular response to exercises. This study investigates human heart rate response dynamics to moderate exercise. A healthy male subject has been asked to walk on a motorised treadmill under a predefined exercise protocol. ECG, body movements, and oxygen saturation (SpO2) have been reliably monitored and recorded by using non-invasive portable sensors. To reduce heart rate variation caused by the influence of various internal or external factors, the designed step response protocol has been repeated three times. Experimental results show that both steady state gain and time constant of heart rate response are not invariant when walking speed is faster than 3 miles/hour, and time constant of offset exercise is noticeably longer than that of onset exercise

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

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. © International Federation for Medical and Biological Engineering 2009

Validierung eines mobilen Sensorsystems zur Abschätzung des Energieumsatzes bei Erwachsenen

Die vorliegende Studie validiert ein mobiles Multisensorsystem, welches physiologische Parameter und Beschleunigungsdaten eines Probanden aufzeichnet. Ein eigens erstelltes Berechnungsmodell für den Energieumsatz, auf Grundlage der erhobenen Daten, wurde dem gemessenen Energieumsatz (Referenz: indirekte Kalorimetrie) gegenübergestellt. Die Parameter und ein vektorbasiertes Verfahren der dreidimensionalen Beschleunigungsdaten zeigen im Berechnungsmodell einen hohen statistischen Zusammenhang zum Energieumsatz. Somit ist die Vorhersage des Energieumsatzes für die gegebenen Bedingungen möglich

Prediction of Oxygen Uptake and its Dynamics by Wearable Sensors During Activities of Daily Living

The evaluation of the aerobic response to a new energetic demand provides valuable information regarding the functional capabilities of the aerobic system. Abnormal, or impaired aerobic responses may occur before the clinical detection of degenerative disease states, demonstrating a need for the development of tools for the continuous assessment of aerobic system dynamics in real-life scenarios. Current wearable technologies are commonly used to quantify physical activity levels; however, the big data streamed from these devices offer the unique possibility to predict the oxygen uptake (VO2) dynamics during unsupervised activities of daily living (ADL) when calorimetry techniques are not accessible. The evaluation of VO2 dynamics has been associated with physical fitness and might provide insight into changes in health status. The main objective of this thesis was to predict and evaluate the temporal dynamics of the aerobic response during realistic activities. To accomplish this, a series of seven studies that began with observations of VO2 dynamics under standard laboratory conditions, progressed to specified patterns of over-ground walking, and concluded with unsupervised ADL facilitated the development of novel techniques for aerobic system analysis during walking and ADL based on wearable sensors. The variables derived from the wearable sensors were used to create a VO2 predictor based on different machine learning approaches. Predicted VO2 was individually validated by Bland-Altman plot and Pearson’s linear correlation coefficient (r). The VO2 dynamic analysis included Fourier transformations, exponential data modeling, and a novel approach derived from the mean normalized gain amplitude (MNG in %). This new indicator of VO2 dynamics correlated strongly with the standard method obtained, in the same subjects, from a step change in work rate on the cycle ergometer, and with the classical indicator of fitness, maximal VO2. The results showed the strong ability of the proposed algorithms to predict VO2 during ADL based on wearable sensors allowing for not only overall assessment of metabolic rate but also for successful prediction of VO2 dynamics. Thus, the proposed VO2 predictor in conjunction with MNG can be used to investigate aerobic fitness during ADL with direct applicability for the general population. This new technology provides a significant advance in ambulatory and continuous assessment of aerobic fitness with potential for future applications such as the early detection of deterioration in physical health