Why measure human failures and errors?

Before addressing failures and errors, it is important to highlight the concept of human factors as approached by Dersalis. According to Grattan (2018), human factors can be understood as anything that interferes with the relationship between the supply and demand of labor. These factors—linked to physical, physiological, and social characteristics—affect human interaction with equipment, systems, processes, and other individuals or work teams. Therefore, they must be measured so that actions can prevent or eliminate risky operational behavior.

Across all productive sectors, fatigue is the primary element that reduces operator performance. It alters key signs and symptoms, such as reaction time, attention or focus, short-term memory, and even the ability to make judgments.

That is why monitoring human behavior is so important—by measuring the probability of human error associated with tasks, especially in challenging work scenarios such as long shifts, irregular hours, night work, repetitive tasks, or exposure to prolonged stress.

The analysis of human error and worker fatigue has been widely studied through subjective and objective methods. Among them, psychomotor vigilance tests and questionnaires stand out. However, their results are often highly sensitive to subjective bias and task interference. According to Armario et al. (2020), although psychometric tests can assess anxiety or stress levels, the responses may deviate significantly from actual biological responses, as individuals tend to adopt coping strategies that influence test results.

According to Armario et al. (2020), alternative approaches for measuring human error lie in the early identification of stressors. When job demands exceed physiological and mental coping capacity, the endocrine and central nervous systems activate the release of physiological markers. These markers can reflect an individual’s stress response and indicate the intensity of the stressor.

That is why it is essential to monitor both external factors (heat, vibration) and internal ones (cognitive preparation, physical conditioning, mental health, illnesses), as these contribute to the balance—or imbalance—of the individual during decision-making processes.

When any of these factors exceed safe thresholds, the human body may present specific responses for adaptation (real or perceived) and coping (passive or active). However, this increases risk exposure and the likelihood of human error.

As pointed out by Burlacu et al. (2021), heart rate variability is a potentially valuable marker for drowsiness, fatigue, and stress monitoring, as it reflects changes in the sympathetic and parasympathetic nervous systems.

Furthermore, Sparrow et al. (2019) reinforce that fatigue detection technology is already broad and effective. It allows the identification of fatigue through variations in heart rate variability, ocular measures, and even speech changes. Unlike aptitude tests, which require isolated measurements, continuous monitoring of vital data enables the tracking of fatigue progression over time.

Therefore, the Dersalis solution aims to identify and mitigate the progression of risky human behavior through the monitoring of physiological variables collected during the workday in industrial operations. It is crucial that health, safety, and operational teams collaborate in the creation of alerts to monitor work fronts—forming a human barrier mechanism capable of minimizing the probability of human failures. This aligns with the understanding and application of James Reason’s Swiss Cheese Model, which, according to Larouzee & Le Coze (2020), remains one of the most relevant models due to its systemic foundation and sustained use in high-risk industries.