Fuzzy Estimation of Physical Fitness
Occupational Medicine encompasses issues such as determination of job requirements, assessing individuals, and monitoring their performance over time. The involved complexities and uncertainties in the field of Occupational Medicine motivate us to use the fuzzy methodologies in handling the complicated optimization and decision making problems. As an application example of the developed Robust Fuzzy Estimation Theory, the problem of estimating the physical fitness of an individual, based on intelligent interpretation of ergo-spirometric data, was considered. The process of estimating physical fitness is uncertain due to 1) the uncertainties involved in the measurements of different physiological parameters and 2) the fact that different patients have different physiological behavior toward exercise testing. We considered the development of an expert system that quantifies the physical fitness level
of an individual on a virtual scale ranging from zero to one. Such a quantitative description of physical fitness is useful even for the non-medical-experts without being involved in the complex physiology of exercise testing. Further, the functionality of the expert system is clearly understood by the user. |
Related Publications
- M. Kumar, R. Stoll, and N. Stoll, “Robust adaptive fuzzy identification of time-varying processes with uncertain data. Handling uncertainties in the physical fitness fuzzy approx- imation with real world medical data: An application,” Fuzzy Optimization and Decision Making, vol. 2, no. 3, pp. 243-259, Sep. 2003.
- M. Kumar, R. Stoll, and N. Stoll, “Regularized adaptation of fuzzy inference systems. Modelling the opinion of a medical expert about physical fitness: An application,” Fuzzy Optimization and Decision Making, vol. 2, no. 4, pp. 317-336, Dec. 2003.
- M. Kumar, R. Stoll, and N. Stoll, “Robust solution to fuzzy identification problem with uncertain data by regularization. Fuzzy approximation to physical fitness with real world medical data: An application,” Fuzzy Optimization and Decision Making, vol. 3, no. 1, pp. 63-82, Mar. 2004.
- M. Kumar, R. Stoll, and N. Stoll, “Robust adaptive identification of fuzzy systems with uncertain data,” Fuzzy Optimization and Decision Making, vol. 3, no. 3, pp. 195-216, Sep. 2004.