Modeling and predicting heart rate dynamics across a broad range of transient exercise intensities during cycling

Mazzoleni, Michael J.; Battaglini, Cláudio L.; Martin, Kerry J.; Coffman, Erin; Mann, Brian P.

doi:10.1007/s12283-015-0193-3

Cited by 19 publications

(13 citation statements)

References 33 publications

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“…On the other hand, the model of heart rate dynamics during cycling [15] has been successfully applied to heart rate time series data providing numerical values for the model's parameters; however, two more model parameters have been introduced. Finally, even though the improved and advanced version of the model of [20] is capable of following the heart rate dynamics for any linear or nonlinear exercise intensity, its ability to fit heart rate time series data, as well as to simulate and predict heart rate responses, has only been shown for exercises of constant intensity [24].…”

Section: Discussionmentioning

confidence: 99%

“…Other models using first order lineal approximations have also been proposed (see [15] and the references therein); however, the implementation of these models is limited, as they can only be applied to exercises of constant intensity.…”

Section: Introductionmentioning

confidence: 99%

“…This model can be implemented for exercise of linear or non-linear intensities, of any functional form. Another recently proposed model of heart rate dynamics during cycling [15] is based on the dynamical systems model of [20] and simulates heart rate dynamics across non-linear exercise intensities by introducing a differential equation for the rate of change of heart rate demand. According to this model, the rate of change of D is assumed to depend on the current value of D, as well as the power output and cadence, and two more model parameters are introduced.…”

Section: Introductionmentioning

confidence: 99%

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Simulating heart rate kinetics during incremental and interval training

Zakynthinaki

2016

Biomedical Human Kinetics

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SummaryStudy aim: To apply a recently proposed dynamical systems model to simulate, for the first time, the heart rate (HR) response to exercise of time-dependent intensity. Material and methods:The applied model is expressed in the form of two coupled ordinary differential equations, one giving the rate of change of HR and the other providing the time dependency of exercise intensity. According to the model, the HR is assumed to depend on time, velocity, lactate accumulation and the subject's overall cardiovascular condition. For the purposes of the present work, training sessions were simulated, consisting of exponentially and step-wise increasing intensities, as well as interval training. Results: Training sessions of exponentially and step-wise increasing velocity, as well as an interval training session, have been simulated. Successful simulations of the corresponding HR response were achieved. Conclusions: The present work successfully demonstrates the model's excellent performance in simulating the HR response to exercises of time-dependent intensity. The applied model has been shown to correctly simulate the heart rate response also during exercises of complicated intensity patterns, such as the interval training session. The study confirms the ability of the implemented mathematical model not only to simulate and predict heart rate kinetics for any given exercise intensities but also to provide important information regarding an individual's cardiovascular condition. This is of vast importance, not only in the area of fitness and sport, where it can serve as a fundamental tool for the design of efficient training sessions, but also in the areas of cardiovascular health, prediction and rehabilitation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simulating heart rate kinetics during incremental and interval training

Zakynthinaki

2016

Biomedical Human Kinetics

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“…Approximation accuracy is analyzed only visually. Improved versions of this model with less parameters were presented by Zakynthinaki ( 2015 ) and Mazzoleni et al ( 2016 ); we will describe their work in section 3.1.5.…”

Section: Modeling and Prediction Of Heart Ratementioning

confidence: 99%

Measurement, Prediction, and Control of Individual Heart Rate Responses to Exercise—Basics and Options for Wearable Devices

et al. 2018

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The use of wearable devices or “wearables” in the physical activity domain has been increasing in the last years. These devices are used as training tools providing the user with detailed information about individual physiological responses and feedback to the physical training process. Advantages in sensor technology, miniaturization, energy consumption and processing power increased the usability of these wearables. Furthermore, available sensor technologies must be reliable, valid, and usable. Considering the variety of the existing sensors not all of them are suitable to be integrated in wearables. The application and development of wearables has to consider the characteristics of the physical training process to improve the effectiveness and efficiency as training tools. During physical training, it is essential to elicit individual optimal strain to evoke the desired adjustments to training. One important goal is to neither overstrain nor under challenge the user. Many wearables use heart rate as indicator for this individual strain. However, due to a variety of internal and external influencing factors, heart rate kinetics are highly variable making it difficult to control the stress eliciting individually optimal strain. For optimal training control it is essential to model and predict individual responses and adapt the external stress if necessary. Basis for this modeling is the valid and reliable recording of these individual responses. Depending on the heart rate kinetics and the obtained physiological data, different models and techniques are available that can be used for strain or training control. Aim of this review is to give an overview of measurement, prediction, and control of individual heart rate responses. Therefore, available sensor technologies measuring the individual heart rate responses are analyzed and approaches to model and predict these individual responses discussed. Additionally, the feasibility for wearables is analyzed.

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“…There are three possible equilibria hr(u,t) for the heart rate of an individual: the resting heart rate hr(u,t)=hr min , the maximum heart rate hr(u,t)=hr max , and the heart rate demand hr(u,t)= D(u,t), which is related to exercise intensity u (speed for treadmill, power for cycle ergometer) [6][7][8][9][10]. This equilibrium can be used to construct a model of heart rate dynamics using the following differential equation consisting of a product of three parts…”

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confidence: 99%

Dynamic models of some physiological parameters in response to exercise

Štork

Novák

Zeman

2017

2017 International Conference on Applied Electronics (AE)

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The study of physiological parameters dynamic is currently the main area of research in exercise physiology. Finding dynamical models of heart rate, oxygen uptake, pulmonary ventilation and other parameters is fundamental for training methodology in sport, as well as for our knowledge of cardiorespiratory health. The present work demonstrates the application of dynamic systems models to the simulation of heart rate kinetics and oxygen consumption during workloads of time-varying intensity. The use of modern mathematical methods of analysis such as the model parameters estimation could be beneficial for understanding the relationship between the physiological responses to load and/or athletic performance, for identifying certain features in the physiological time series. As a result, the application of such methods for analysis and modeling will have a large impact not only on the development and better understanding training methodology and the testing data of athletes but also in the area of exercise medicine.

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Modeling and predicting heart rate dynamics across a broad range of transient exercise intensities during cycling

Cited by 19 publications

References 33 publications

Simulating heart rate kinetics during incremental and interval training

Simulating heart rate kinetics during incremental and interval training

Measurement, Prediction, and Control of Individual Heart Rate Responses to Exercise—Basics and Options for Wearable Devices

Dynamic models of some physiological parameters in response to exercise

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