Edit Tóth-Laufer

Conjunction and disjunction operators in neuro-fuzzy risk calculation model simplification

In this paper a possible simplification of a risk level calculation models neuro-fuzzy subsystem will be studied. The basic model has a hierarchical multilevel structure and uses fuzzy logic based decision making for some groups of risk factors and neuro-fuzzy subsystem is based on ANFIS model structure for the other. The simplification of neuro-fuzzy subsystem is based on disjunction fuzzy operator where the operator selection is application-dependent. This is because the three basic operations of crisp sets (negation, conjunction, and disjunction) can be generalized for fuzzy sets in an infinite number of ways. In this work the effect of different conjunction and disjunction operators on the result of simplified structure was compared and analyzed.

Anytime sport activity risk level calculation using HOSVD based hierarchical fuzzy models

In this paper a fuzzy logic-based risk calculation model is introduced, which is used to assess the risk level of sport activity in real-time. In these kinds of systems the computational complexity is a key factor, because the sufficiently accurate results should be available in time. The aim is to find the balance between the computational complexity and the accuracy. Anytime techniques are well-suited for these types of problems, because the combination of the soft computing and anytime algorithms can cope with the dynamically changing and possible insufficient amount of resources and reaction time and it is able to adaptively work with the available information which is usually imperfect or even missing. In this study the Singular Value Decomposition (SVD)-based algorithm is used to reduce the basic fuzzy model complexity.

Risk level calculation for body physical exercise with different fuzzy based methods

In this paper two types of hierarchical multilevel models will be introduced for the calculation of risk of physical exercise in fuzzy environment. One of them is the Analytic Hierarchy Process with Fuzzy Comprehensive Evaluation based model and the other was made in Simulink-Fuzzy Toolbox environment with Mamdani-type fuzzy evaluation. The two methods were tested on several input data and the result of them has been compared and analyzed.

A Soft Computing-Based Hierarchical Sport Activity Risk Level Calculation Model for Supporting Home Exercises

With the spread of active styles of living, regular health monitoring and risk estimation of exercises became an essential part of everyday life. In this paper, a fuzzy logic-based hierarchical, classified risk calculation model is introduced, which can be used to assess the risk level of sport activity in real-time. The model considers the current physical status and the preliminary assessed medical conditions of the person, the activity load of the exercise, as well as the environmental conditions. Based on this information, a hierarchical fuzzy decision making system evaluates the risk level and sends warning (to the person to stop the activity) or alerting (to a medical doctor/hospital) or both if necessary. By this, serious health problems/crisis situations can be avoided. The complexity of the model is optimized by the application of the singular value decomposition-based complexity reduction algorithm. In critical situations [when the available (dynamically changing) amount of time, resources, and data become insufficient], the anytime mode of operation helps to cope with the temporal conditions and to find a tradeoff between the computational complexity of the evaluations and the accuracy of the results. The system can be operated real-time at home, thus making more comfortable and safer the active (preventive) life and rehabilitation processes of conscious people.

The effect of aggregation and defuzzification method selection on the risk level calculation

In this paper a fuzzy logic-based hierarchical multilevel risk calculation model will be introduced with different model parameters. On each occasion when a fuzzy-based simulation model is constructed, the appropriate aggregation and defuzzification method must be chosen. It is very difficult because it cannot be said generally, which is the best method, its depends on the current application. The model presented in the paper is a model for risk calculation of physical exercise, and it was constructed in Simulink - Fuzzy Logic Toolbox environment with Mamdani-type fuzzy evaluation and different aggregation and defuzzification operators. The test was performed for several typical groups of the patients. The results are compared with a previously implemented Analytic Hierarchy Process with Fuzzy Comprehensive Evaluation based model with similar purposes. The result of the comparison has been analyzed and the best methods have been selected.

Fuzzy logic-based sport activity risk assessment framework optimization

The aim of the system presented in this paper is to assess the current risk level based on personal parameters during sport activity. In these kinds of monitoring systems the patient-specific evaluation is essential to obtain realistic result thereby making use of the system more secure. This requirement is fulfilled by the risk assessment framework published by the author and her colleagues. However, it is also equally important to obtain the accurate result in time to avoid the serious consequences. In order to address this problem in this paper the author presents some evaluation structure reduction techniques which reduce the computational need of the risk assessment. These models are the modifications of the conventional Mamdani-type inference system maintaining its advantageous properties while the computational complexity is reduced and the result is equivalent to the conventional system result.

Personal-Statistics-Based Heart Rate Evaluation in Anytime Risk Calculation Model

Nowadays, the changes in peoples habits and the development of technology result in the wide spreading of the model-based health monitoring systems. In this paper, an improvement of the measurement evaluation method is introduced, which is used during the sport activity in real-time. The basis of the novel approach is our previously reported anytime hierarchical fuzzy risk calculation model, which is able to handle some uncertainties, imprecision, and subjectivity in the data and in the evaluation process and can cope with the dynamically changing environment, available time, and resources. In the new model the input membership functions, which are tuned according to the patient characteristics, are modified based on the data recorded during previous measurements under the same conditions. By this, the person-dependent characteristics and the unavoidable changing of the dynamic reactions of the human organism can also be considered and the risk level can be more reliably predicted.

Database schema design for supporting sport activity monitoring

The patient-specific evaluation in sport monitoring is the basis of obtaining realistic results. It means that the risk factors should be well chosen from the patients as well as the chosen sport types point of view, the interactions between the factors should be mapped and the thresholds should be defined, depending on the patient for each sport type separately. In order to manage these requirements a robust, flexible risk assessment framework is needed with high adaptive capacity. This framework is effectively applicable only in the case if a properly constructed database serves as the basis of it. In this paper the author presents a database schema design to support the implementation of this kind of sport activity risk assessment framework, which uses fuzzy logic to handle the uncertainties, imprecision and subjectivity in the system parameters and in the evaluation process. The database designing problems and their treatment possibilities are also being considered in the paper.

A personal profile based patient-specific anytime risk calculation model

Nowadays, the significance of model based health monitoring systems has grown. In this paper, an improved realtime sport activity risk calculation model is introduced. The new approach is based on the previously reported anytime hierarchical fuzzy risk calculation model of the authors which is able to handle some uncertainties, imprecision, and subjectivity in the data and in the evaluation process and can cope with the dynamically changing environment, available time, and resources. The new model is extended with further patient-specific features like tuned membership functions applied during the evaluation and data recorded during previous measurements. Furthermore, the authors propose a new pre-processing procedure as well, which allows comparing the currently measured values to the stored data recorded under the same conditions. By this, the person-dependent characteristics and the unavoidable changing of the dynamic reactions of the human organism can also be considered and the risk level can more reliable be predicted.

Error calculation of the HOSVD-based rule base reduction in hierarchical fuzzy systems

Abstract In real time evaluation systems the decision should be made in time to avoid the serious consequences. In the literature there are several techniques which aim to reduce the computational complexity of these kinds of systems. Among others the Higher Order Singular Value Decomposition (HOSVD) based reduction method is a useful tool for handling this problem. In the case when only non-exact reduction can be performed, the error calculation is also essential, because the reduction degree is based on the calculated error in these systems. In this paper the authors present the HOSVD reduction error calculation in hierarchical fuzzy systems using Mamdani-type inference and define a general formula which includes the propagated error from the previous levels of the hierarchy; in which the error bound, calculated by different ways, can be used and takes into account the case when not all the inputs contain a propagated error but only a few of them. Furthermore a greedy algorithm is presented for reduction optimization.