Research of Human Fatigue and Measurement Parameters for Workability Assessment

Abstract

<jats:p>Human fatigue is reduced working capability for certain period of time as the result of unusual or prolonged workload. Fatigue arises when the body’s energy requirements exceed its supply. Fatigue first manifests as reduced concentration capability causing movement coordination and precision disruption leading to decreasing workability. Fatigue is an object of research in physiology, psychology, work ergonomics, medicine, and biotechnology where each domain has a focus on mental fatigue. The functional state in the context of professional activity is defined as a complex of characteristics of functions and qualities that determine the fulfilment of labour activity. Furthermore, a comprehensive estimation of subject functional state in combination with other factors like subject self-assessment and objective performance tests (cognitive load tests) is a necessary input for the evaluation of workability and efficiency on task. The heterogeneous nature of fatigue as a systemic manifestation requires analysis of multiple key parameters which are relevant to the specific type. The current feasibility study focuses on human biological signal from electrical activity of heart, brain, muscles and skin potentials as well as temperature, position, and respiration to obtain diagnostic parameters reflecting the state of cardiovascular, muscles, and central nervous systems for physiological monitoring of vital signs. The fatigue physiological parameter and feature formalization aim to support the development of a platform with complex passive multi-level fatigue monitoring system and workability evaluation system designed in order to provide an integrated service.</jats:p>

Methodology for selection and decision making of gradation of sleepiness using algorithms for face video image processing

“…The application of dichotomy, in contrast to the majority of previously observed methods, allows to further extend the classification gradations; in this work it provides significant results with three output groups of sleepiness state. Based on authors' previous work on parameter selection [2], the sleepiness detection methods can be divided into four groups of observed data -physiological, activity and subjective features. The current paper focuses on the in-depth analysis and extraction of behavioural domain features to supplement the data acquisition in a decision support system.…”