What is fatigue?
Fatigue is a complex clinical condition involving behavioral, emotional, and cognitive factors. It presents as a persistent sense of tiredness that directly affects a person's physical and mental performance.
In practice, fatigue represents a reduced ability to perform tasks safely and attentively — whether due to sleep deprivation, prolonged wakefulness, circadian rhythm disruptions, or mental/physical workload.
The main challenge is that the individual often does not realize they are fatigued, as the symptoms can be subtle and difficult to detect clinically.
Why does it matter?
Sleep is essential to maintaining physical and mental health. When insufficient, it becomes a public health issue. Short sleep duration has been linked to several negative health outcomes, including cardiovascular issues, obesity, workplace accidents and errors, lower quality of life, and increased mortality.
Fatigue is often the result of poor sleep practices. It may also lead to daytime drowsiness, accompanied by symptoms such as irritability, lack of concentration, attention deficits, reduced vigilance, distraction, low motivation, discomfort, low energy, and restlessness.
The Fatigue Module aims to offer alternatives for measuring human error, with the goal of identifying early signs of drowsiness — a transitional state that can impair the balance between task demands and the worker’s physiological and/or cognitive capacity.
How does fatigue affect work?
Daytime drowsiness is one of the most common symptoms of fatigue. It is associated with:
Attention deficits
Reduced vigilance
Distraction
Lack of motivation
Discomfort
Low energy
Restlessness
These effects compromise the ability to perform critical tasks such as operating machinery, driving, or making decisions under pressure.
Common causes in industrial environments
Fatigue often stems from a mix of external and internal factors:
Long work shifts
Rotating schedules
Sleep deprivation
Excessive noise
Constant vibration
Extreme temperatures
Inadequate lighting
Poor physical or mental conditioning
Together, these factors turn fatigue into a serious operational risk — both for the worker and the company.
What does the Dersalis Fatigue Module do?
The module is designed to identify signs of drowsiness early, before they become an operational hazard.
It continuously monitors physiological variables throughout the workday, detecting changes in the autonomic nervous system — especially by analyzing heart rate variability.
These measurements reveal patterns of parasympathetic activity, which are closely associated with fatigue.
How to configure the Fatigue Module
When creating or editing a monitoring profile, the following options are available:
Reference value:
Automatic alert triggered by the Parasympathetic Activity Detection Algorithm ®Alert recurrence options:
None
5 minutes
10 minutes
15 minutes
Suggested use:
Recommended for professionals performing monotonous tasks, such as vehicle or equipment operators
Operational advantages
Fatigue directly impacts the two pillars of operational performance: productivity and safety.
With the Dersalis Fatigue Module, your company benefits from a smart monitoring system that detects risks before incidents occur, using real-time physiological data — without disrupting task execution.
Managing fatigue in practice
Dersalis analyzes physiological variables throughout the workday, detecting changes in the autonomic nervous system. Through heart rate variability, it identifies fatigue based on dominant parasympathetic activity.
This data enables the platform to support the implementation of barrier mechanisms and intervention strategies, helping to mitigate the development of human-risk behavior.
Shift workers — especially those operating vehicles or equipment — should receive proper guidance on prevention, alert response, and how to use Dersalis devices in line with internal safety policies.
By Dr. André R. Soares
Physician | Dersalis
References
GAO, R. et al. Study on the nonfatigue and fatigue states of orchard workers based on electrocardiogram signal analysis. Scientific Reports, 12(1), 1–17, 2022. https://doi.org/10.1038/s41598-022-08705-z
Rahma, O. N., & Rahmatillah, A. (2019). Drowsiness Analysis Using Common Spatial Pattern and Extreme Learning Machine Based on Electroencephalogram Signal.
Kao, I-H., & Chan, C-Y. (2022). Comparison of Eye and Face Features on Drowsiness Analysis. Sensors, 22(17), 6529.
