Physiological Control of Human Heart Rate and Oxygen Consumption during Rhythmic Exercises

Physical exercise has significant benefits for humans in improving the health and quality of their lives, by improving the functional performance of their cardiovascular and respiratory systems. However, it is very important to control the workload, e.g. the frequency of body movements, within the capability of the individual to maximise the efficiency of the exercise. The workload is generally represented in terms of heart rate (HR) and oxygen consumption (VO2). We focus particularly on the control of HR and VO2 using the workload of an individual body movement, also known as the exercise rate (ER), in this research. The first part of this report deals with the modelling and control of HR during an unknown type of rhythmic exercise. A novel feature of the developed system is to control HR via manipulating ER as a control input. The relation between ER and HR is modelled using a simple autoregressive model with unknown parameters. The parameters of the model are estimated using a Kalman filter and an indirect adaptive H∞ controller is designed. The performance of the system is tested and validated on six subjects during rowing and cycling exercise. The results demonstrate that the designed control system can regulate HR to a predefined profile. The second part of this report deals with the problem of estimating VO2 during rhythmic exercise, as the direct measurement of VO2 is not realisable in these environments. Therefore, easy-to-use sensors are used to non-invasively measure HR, RespR, and ER to estimate VO2. The developed approach for cycling and rowing exercise predicts the percentage change in maximum VO2 from the resting to the exercising phases, using a Hammerstein model. These VO2 estimators were validated on six subjects by comparing the measured and estimated values of VO2 for quality of fit. Results show that the average quality of fit in both exercises is improved as the intensity of exercise is increased. This shows that the relation between ER, RespR, HR and VO2 is highly correlated during high-intensity exercise. Consequently, a selfbiofeedback (SBF) control of VO2 is implemented in real time and the experimental results show the effectiveness of the proposed approach. Moreover, the efficiency of the SBF system is also analysed to show that it is more efficient in controlling VO2