Predicting Human Reliability based on Individual’s Resting Period: Effect of Physical Workload Rate
Main Article Content
Keywords
human performance, Human reliability, physical workload, human error, cognitive performance
Abstract
When a person is exposed to a prolonged workload, he/she enters a fatigue phase, the indication is the decline of cognitive performance that leading to human error. As an integral part of a system, human contributes to system reliability; therefore, it plays an important role in potential failure. Those, it is necessary to investigate how human reliability relates to physical workload rate, in order to predict maximum work duration to eliminate potential failure. A physical experiment involving 20 participants was conducted to generate medium workload, followed by Stroop test to observe selective attention and cognitive control as a form of cognitive performance. The physical workload was observed through energy expenditure and oxygen consumption during physical activity, and cognitive performance through response error time on the Stroop test. The usage of Weibull distribution was aimed to obtain reliabilities for each participant. There was a decline in reliability for all participants from one test to the other. Based on scale and form parameters, the prediction of resting time was based on mean time to human error (MTTHE), and from this experiment, varied MTTHE from each participant were obtained. The variation was created by differences in physical performance, cognitive capabilities, and other contributing factors such as environment and time of the implementation of the experiment. From this research, it was evident that human reliability can be utilized to predict potential failure in humans, which then implies a preventive action is necessitated to prevent failure from manifesting in the shape of taking a break/rest or reducing work rhythm. The application of human reliability in human resource management can be directed towards fatigue management and operator-related operational management.
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