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The aim of this paper is to study the human Heart Rate (HR) response during walking, cycling and rowing exercises using linear time varying (LTV) models. We used the frequency of exercise locomotion as the input to the model. This frequency characterizes the stride rate, cadence rate and strokes rate of the walking, cycling and rowing exercises respectively. The time varying parameters in the LTV models were estimated by the Kalman Filter (KF). The results in this study demonstrate that HR responses to these exercises exhibit some degree of time varying nature.

A system identification of a two-wheeled robot (TWR) using a data-driven approach from its fundamental nonlinear kinematics is investigated. The fundamental model of the TWR is implemented in a Simulink environment and tested at various input/output operating conditions. The testing outcome of TWR’s fundamental dynamics generated 12 datasets. These datasets are used for system identification using simple autoregressive exogenous (ARX) and non-linear auto-regressive exogenous (NLARX) models. Initially the ARX structure is heuristically selected and estimated through a single operating condition. We conclude that the single ARX model does not satisfy TWR dynamics for all datasets in term of fitness. However, NLARX fitted the 12 estimated datasets and 2 validation datasets using sigmoid nonlinearity. The obtained results are compared with TWR’s fundamental dynamics and predicted outputs of the NLARX showed more than 98% accuracy at various operating conditions.

The aim of the study is to regulate the human heart rate (HR) response to a pre-defined reference profile during aerobic activities of unknown type. A novel feature of the designed control system is obtained to generate the desired rhythmic movements, which is required to achieve the target HR profile during aerobic activities of unknown type. These rhythmic movements or frequency of locomotion is known as the exercise rate (ER) and is quantified as a fundamental measure of exercise intensity. The relationship between ER and HR is modelled by using a Linear Time Varying (LTV) system. The parameters of the model are estimated using a Kalman Filter. Based on this model, a robust adaptive H ∞ controller is designed. The H ∞ controller generates target ER (ER T) corresponding to target HR (HR T). This ER T is communicated to the exercising subject by using a human actuating System (HAS). The role of HAS is to achieve (ER T). To validate the performance of the system, it is tested on 6 healthy subjects during rowing and cycling exercises. The results demonstrate that the designed control system can regulate HR at a given profile with an average root mean square error (RM SE) of 3.1857 bpm and 2.9396 bpm for rowing and cycling, respectively. The developed system can be used for designing an optimal exercising protocol for individuals.