Universal algorithm for exercise rate estimation in walking, cycling and rowing using triaxial accelerometry

A technique that can reliably monitor exercise intensity plays an important role for the effectiveness and safety of an exercise prescription. A universal algorithm for the recursive estimation of exercise rate during a variety of aerobic exercises using measurements from a body-mounted triaxial accelerometer (TA) is proposed. Information about the type of exercise is not required by the algorithm and the TA can be mounted at the same location regardless of the exercise type. The algorithm involves period detection and data fusion. Experimental results demonstrate that the algorithm is effective for common aerobic exercises. © The Institution of Engineering and Technology 2009

A nonlinear dynamic model for heart rate response to treadmill walking exercise

A dynamic model of the heart rate response to treadmill walking exercise is presented. The model is a feedback interconnected system; the subsystem in the forward path represents the neural response to exercise, while the subsystem in the feedback path describes the peripheral local response. The parameters of the model were estimated from 5 healthy adult male subjects, each undertaking 3 sets of walking exercise at different speeds. Simulated responses from the model closely match the experimental data both in the exercise and the recovery phases. The model will be useful in explaining the cardiovascular response to exercise and in the design of exercise protocols for individuals. © 2007 IEEE

Exercise rate estimation using a triaxial accelerometer

In this paper, we propose an algorithm for the estimation of exercise rate during a variety of exercises by using measurements from triaxial accelerometry. The algorithm involves the detection of the periodicity of the body's accelerations, and the detected periods are then fused to form an estimate of exercise rate. Experimental results demonstrate that the algorithm is effective in different modes of exercise. The proposed algorithm will be useful in monitoring training exercises for healthy individuals and rehabilitation exercises for cardiac patients. ©2009 INSTICC - Institute for Systems and Technologies of Information, Control and Communication

Nonlinear modelling and control of heart rate response to treadmill walking exercise

In this study, a nonlinear system was developed for the modelling of the heart rate response to treadmill walking exercise. The model is a feedback interconnected system which can represent the neural response and peripheral local response to exercise. The parameters of the model were identified from an experimental study which involved 6 healthy adult male subjects, each completed 3 sets of walking exercise at different speeds. The proposed model will be useful in explaining the cardiovascular response to exercise. Based on the model, a 2-degree-of-freedom controller was developed for the regulation of the heart rate response during exercise. The controller consists of a piecewise LQ and an H∞, controllers. Simulation results showed that the proposed controller had the ability to regulate heart rate at a given target, indicating that the controller can play an important role in the design of exercise protocols for individuals

Nonlinear modeling of cardiovascular response to exercise

This study experimentally investigates the relationships between central cardiovascular variables and oxygen uptake based on nonlinear analysis and modeling. Ten healthy subjects were studied using cycle-ergometry exercise tests with constant workloads ranging from 25 Watt to 125 Watt. Breath by breath gas exchange, heart rate, cardiac output, stroke volume and blood pressure were measured at each stage. The modeling results proved that the nonlinear modeling method (Support Vector Regression) outperforms traditional regression method (reducing Estimation Error between 59% and 80%, reducing Testing Error between 53% and 72%) and is the ideal approach in the modeling of physiological data, especially with small training data set

Portable sensor based dynamic estimation of human oxygen uptake via nonlinear multivariable modelling

Noninvasive portable sensors are becoming popular in biomedical engineering practice due to its ease of use. This paper investigates the estimation of human oxygen uptake (VO2) of treadmill exercises by using multiple portable sensors (wireless heart rate sensor and triaxial accelerometers). For this purpose, a multivariable Hammerstein model identification method is developed. Well designed PRBS type of exercises protocols are employed to decouple the identification of linear dynamics with that of nonlinearities of Hammerstein systems. The support vector machine regression is applied to model the static nonlinearities. Multivariable ARX modelling approach is used for the identification of dynamic part of the Hammerstein systems. It is observed the obtained nonlinear multivariable model can achieve better estimations compared with single input single output models. The established multivariable model has also the potential to facilitate dynamic estimation of energy expenditure for outdoor exercises, which is the next research step of this study. © 2008 IEEE

Optimizing heart rate regulation for safe exercise

Safe exercise protocols are critical for effective rehabilitation programs. This paper aims to develop a novel control strategy for an automated treadmill system to reduce the danger of injury during cardiac rehabilitation. We have developed a control-oriented nonparametric Hammerstein model for the control of heart rate during exercises by using support vector regression and correlation analysis. Based on this nonparametric model, a model predictive controller has been built. In order to guarantee the safety of treadmill exercise during rehabilitation, this new automated treadmill system is capable of optimizing system performance over predefined ranges of speed and acceleration. The effectiveness of the proposed approach was demonstrated with six subjects by having their heart rate track successfully a predetermined heart rate. © 2009 Biomedical Engineering Society

Analysis of factors influencing the modelling of occupant window opening behaviour in an office building in Beijing, China.

This paper introduces a longitudinal study monitoring occupants’ window opening behaviour in a mixed-mode office building in Beijing, China, when natural ventilation is specifically used for controlling the building’s indoor thermal environment. Based on the field measured data, the influence of factors, including outdoor air temperature, outdoor PM2.5, indoor air temperature, time of day, occupancy and previous window state, on the observed state of windows is analysed. All of them are influential on occupants’ window opening behaviour in the case study building, and so they can be used to model occupants’ window opening behaviour in buildings in China to achieve a better consideration of occupant behaviour in dynamic building performance simulation

Comparison of analyses of the QTLMAS XII common dataset. I: Genomic selection

<p>Abstract</p> <p>A dataset was simulated and distributed to participants of the QTLMAS XII workshop who were invited to develop genomic selection models. Each contributing group was asked to describe the model development and validation as well as to submit genomic predictions for three generations of individuals, for which they only knew the genotypes. The organisers used these genomic predictions to perform the final validation by comparison to the true breeding values, which were known only to the organisers. Methods used by the 5 groups fell in 3 classes 1) fixed effects models 2) BLUP models, and 3) Bayesian MCMC based models. The Bayesian analyses gave the highest accuracies, followed by the BLUP models, while the fixed effects models generally had low accuracies and large error variance. The best BLUP models as well as the best Bayesian models gave unbiased predictions. The BLUP models are clearly sensitive to the assumed SNP variance, because they do not estimate SNP variance, but take the specified variance as the true variance. The current comparison suggests that Bayesian analyses on haplotypes or SNPs are the most promising approach for Genomic selection although the BLUP models may provide a computationally attractive alternative with little loss of efficiency. On the other hand fixed effect type models are unlikely to provide any gain over traditional pedigree indexes for selection.</p