Fuzzy Logic Modelling-Based Measurement Approach for Mental Stress Measurement

Introduction

Mental-stress has been regarded as an important aspect that can affect individuals' performances. It affects the performance of human-beings. Due to higher levels of mental-stress, people are unable to complete complex tasks or assigned job efficiently. The stresses in workplaces affect the physical as well as mental conditions of the workers. The unemployment problem, crises of money, physical health issues, Loneliness, can lead to work-related stresses (Hovey et al. 2006). Particularly in America, the farming occupation is more dangerous and stressful, Fetsch and Schultz (1984). Usually, farmers have financial problems and the farmers often work on their own for long working-hours with untrustworthy equipment and in a hard climate, with hazardous-chemical and live-stock. In addition, they make large financial-commitments that expose them to a higher-level of financial-risks. With the occurrences of these stresses that cannot be effectively handled, both mental in addition to physical changes may occur in human-body. Moreover, an increasing use of fuzzy-based models is taking place in the current industrial and academic researches and applications, mostly for data-analysis and systems’ control, such as for congestion-control in wireless-communications network (Sawhney et al., 2014); wind-energy conversion-system (Chakraborty and Barma, 2014); sub pixel edge-detection (Bala and Dhir, 2014); cluster-head selection-protocol (Gajjar et al., 2014); temperature-regulation process (Ramanathan, 2014); and optimum path-planning of mobile-robots (Pandey and Parhi, 2017). However, very limited study was being done with regard to the use of fuzzy-environment for mental-health related issues in the agricultural sectors. Thus, the objective of this study was to identify the key risk-factors in farming work-places in addition to the levels of depressions, anxieties and work-stresses of farmers of Indian agriculture leading to mental ill-health among them, and further to develop a fuzzy-based models considering both these cases in the fuzzy-environment followed by the use of “Genetic Algorithm (GA)” for the evaluation of mental-stress.