Fuzzy Theoretic Analysis for Physiological Assessment of Job Tasks
This study considers a multi-parametric physiological approach to assessment of job tasks of the operators in a modern automated life sciences laboratory by evaluating the physiological state of the operator. The assessment of the physiological state of an operator requires an objective evaluation of physiological measures while taking into account both measurement noise and uncertainties arising from individual factors. We suggest to represent multi-dimensional medical data by means of an optimal fuzzy membership function for mathematically modeling the effect of a job task on the physiological status of the operator. A carefully designed data model is introduced in a completely deterministic framework where uncertain variables are characterized by fuzzy membership functions. The study derives the analytical expressions of fuzzy membership functions on variables of the multivariate data model by maximizing the over-uncertainties-averaged-log-membership values of data samples around an initial guess. The analytical solution lends itself to a practical modeling algorithm facilitating the data classification. The experiments performed on 20 subjects running biological screening processes in a laboratory verified that the proposed method is competing alternative to typically used pattern recognition and machine learning algorithms.