Dynamic spatial weight matrix and localised STARIMA for network modelling

Various statistical model specifications for describing spatiotemporal processes have been proposed over the years, including the space–time autoregressive integrated moving average (STARIMA) and its various extensions. These model specifications assume that the correlation in data can be adequately described by parameters that are globally fixed spatially and/or temporally. They are inadequate for cases in which the correlations among data are dynamic and heterogeneous, such as network data. The aim of this article is to describe autocorrelation in network data with a dynamic spatial weight matrix and a localized STARIMA model that captures the autocorrelation locally (heterogeneity) and dynamically (nonstationarity). The specification is tested with traffic data collected for central London. The result shows that the performance of estimation and prediction is improved compared with standard STARIMA models that are widely used for space–time modeling. En los últimos años, se han propuesto diversas especificaciones de modelado estadístico para describir procesos espacio-temporales. Esto incluye el modelo espacio-temporal autorregresivo integrado de media móvil (STARIMA) y sus varios derivados. Estas especificaciones de modelo asumen que la correlación de los datos puede ser adecuadamente descrita por parámetros que se fijan a nivel global en el espacio y/o tiempo. Dichos parámetros son inadecuados para los casos en los que las correlaciones entre los datos son dinámicas y heterogéneas, como en el contexto de los datos de la red. El objetivo de este artículo es describir la autocorrelación en los datos de red con una matriz de ponderación espacial dinámica y un modelo STARIMA localizado (LSTARIMA) que captura la autocorrelación local (heterogeneidad) de forma dinámica (no estacionariedad). La especificación del modelo es evaluada con datos de tráfico recolectados en el centro de Londres. Los resultados demuestran que los rendimientos de estimación y predicción mejoran con el método propuesto en comparación con los modelos STARIMA estándar que son ampliamente utilizados para el modelado de espacio-temporal

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

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

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

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

A transient homotypic interaction model for the influenza A virus NS1 protein effector domain

Influenza A virus NS1 protein is a multifunctional virulence factor consisting of an RNA binding domain (RBD), a short linker, an effector domain (ED), and a C-terminal 'tail'. Although poorly understood, NS1 multimerization may autoregulate its actions. While RBD dimerization seems functionally conserved, two possible apo ED dimers have been proposed (helix-helix and strand-strand). Here, we analyze all available RBD, ED, and full-length NS1 structures, including four novel crystal structures obtained using EDs from divergent human and avian viruses, as well as two forms of a monomeric ED mutant. The data reveal the helix-helix interface as the only strictly conserved ED homodimeric contact. Furthermore, a mutant NS1 unable to form the helix-helix dimer is compromised in its ability to bind dsRNA efficiently, implying that ED multimerization influences RBD activity. Our bioinformatical work also suggests that the helix-helix interface is variable and transient, thereby allowing two ED monomers to twist relative to one another and possibly separate. In this regard, we found a mAb that recognizes NS1 via a residue completely buried within the ED helix-helix interface, and which may help highlight potential different conformational populations of NS1 (putatively termed 'helix-closed' and 'helix-open') in virus-infected cells. 'Helix-closed' conformations appear to enhance dsRNA binding, and 'helix-open' conformations allow otherwise inaccessible interactions with host factors. Our data support a new model of NS1 regulation in which the RBD remains dimeric throughout infection, while the ED switches between several quaternary states in order to expand its functional space. Such a concept may be applicable to other small multifunctional proteins

Nonparametric Hammerstein model based model predictive control for heart rate regulation.

This paper proposed a novel nonparametric model based model predictive control approach for the regulation of heart rate during treadmill exercise. As the model structure of human cardiovascular system is often hard to determine, nonparametric modelling is a more realistic manner to describe complex behaviours of cardiovascular system. This paper presents a new nonparametric Hammerstein model identification approach for heart rate response modelling. Based on the pseudo-random binary sequence experiment data, we decouple the identification of linear dynamic part and input nonlinearity of the Hammerstein system. Correlation analysis is applied to acquire step response of linear dynamic component. Support Vector Regression is adopted to obtain a nonparametric description of the inverse of input static nonlinearity that is utilized to form an approximate linear model of the Hammerstein system. Based on the established model, a model predictive controller under predefined speed and acceleration constraints is designed to achieve safer treadmill exercise. Simulation results show that the proposed control algorithm can achieve optimal heart rate tracking performance under predefined constraints

A computational framework to emulate the human perspective in flow cytometric data analysis

Background: In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. In particular, the assumption of many common techniques that cell populations could be modeled reliably with pre-specified distributions may not hold true in real-life samples, which can have populations of arbitrary shapes and considerable inter-sample variation. <p/>Results: To address this, we developed a new framework flowScape for emulating certain key aspects of the human perspective in analyzing flow data, which we implemented in multiple steps. First, flowScape begins with creating a mathematically rigorous map of the high-dimensional flow data landscape based on dense and sparse regions defined by relative concentrations of events around modes. In the second step, these modal clusters are connected with a global hierarchical structure. This representation allows flowScape to perform ridgeline analysis for both traversing the landscape and isolating cell populations at different levels of resolution. Finally, we extended manual gating with a new capacity for constructing templates that can identify target populations in terms of their relative parameters, as opposed to the more commonly used absolute or physical parameters. This allows flowScape to apply such templates in batch mode for detecting the corresponding populations in a flexible, sample-specific manner. We also demonstrated different applications of our framework to flow data analysis and show its superiority over other analytical methods. <p/>Conclusions: The human perspective, built on top of intuition and experience, is a very important component of flow cytometric data analysis. By emulating some of its approaches and extending these with automation and rigor, flowScape provides a flexible and robust framework for computational cytomics