On a mine site, risk is elevated long before an operator closes their eyes. The risk begins earlier, during the progressive build-up of drowsiness-related impairment.
Key summary:
- By the time visible signs of drowsiness appear, impairment is often already elevated. Early-stage detection creates the intervention time needed to prevent an accident.
- Reactive drowsiness monitoring leaves mining operations exposed, particularly during night operations, long shifts, and repetitive haulage tasks.
- Objective, real-time monitoring of impairment allows mining operations to move from reactive incident response to predictive, measurable risk management.
In the mining industry, it is common to use the terms ‘fatigue’ and ‘drowsiness’ interchangeably. In this insights piece, we stick to the term commonly used in the scientific literature: ‘drowsiness’.
Mining operations have spent decades building precise measurements around equipment health, environmental hazards, and production performance. Human impairment deserves the same rigour.
In high-risk mining environments, detecting risk early determines whether an intervention prevents a performance failure or frenetically responds to one after the risk has already escalated.
Why mining operations are uniquely exposed to impairment risk
Mining creates conditions where drowsiness-related impairment accumulates progressively across a shift. Long operating hours, night work, circadian disruption, repetitive haulage tasks, isolated environments, and physically demanding schedules all contribute to elevated risk exposure.
The consequences are measurable. According to the International Council on Mining and Metals (ICMM) 2024 Safety Report, mobile equipment was the leading cause of fatalities across global mining operations that year, accounting for 21% of total fatalities.[1] Across the ICMM membership, which represents approximately one third of the global mining industry, mobile equipment has consistently ranked among the top causes of fatality for more than a decade.
The challenge is not simply that operators may become drowsy. Impairment often develops gradually and is not detected until risk has become critical.
As such, impairment directly affects the relative risk of a performance failure. Delayed reaction times and failures to respond to hazards are all measurable consequences. In mining, those failures can be measured in equipment damage, downtime, serious injury, or loss of life.
The problem with reactive detection
Many drowsiness management approaches still rely on reactive indicators: long eyelid closures (LECs), percentage of eye closure (perclos), head nodding, lane deviation, steering corrections, yawning, or self-reported tiredness. These indicators may identify operators who are already significantly impaired. They rarely appear early enough to be useful.
A system that only detects risk once obvious behavioural symptoms emerge is operating at the wrong end of the risk curve. By the time an operator shows clear signs, impairment is likely already affecting performance.
This creates a narrow intervention window. As a result, supervisors receive limited time to implement countermeasures as risk becomes critical.
Self-assessment compounds the problem. Research by the AAA Foundation for Traffic Safety found that 75% of drivers who rated their own drowsiness as low showed objective signs of moderate or severe impairment. Even when drivers recognised they were drowsy, three quarters of them declined available rest opportunities and continued.[2]
In mining, the pressure to appear resilient is common, and self-reporting thus presents a further complication. Workers may underestimate or avoid reporting drowsiness. This can leave operations exposed to elevated risk without any objective way to measure it.
Why early-stage detection changes operational outcomes
Predictive monitoring changes the operational model.
Instead of waiting for visible symptoms, early-stage detection identifies elevated drowsiness before an operator reaches a dangerous state.
This creates time for intervention before a performance failure occurs. Often described as the difference between a reactive fire alarm and a preventative smoke detector, predictive monitoring allows mining operations to act before risk escalates to critical levels.
When supervisors receive objective measurements of escalating impairment earlier in its progression, they can implement proactive countermeasures: task rotation, controlled rest breaks, workload adjustment, or temporary removal from safety-critical activities.
Early-stage visibility also improves decision-making at a broader level. Trend data over time allows operations to understand how specific roster structures, shift transitions, environmental conditions, and work patterns influence impairment risk. Drowsiness becomes measurable. More importantly, it becomes manageable.
Objective measurement removes the guesswork
Terminology matters here.
Drowsiness is a neurological state on the continuum between being awake and asleep.
Impairment is the increase in relative risk of a performance failure. It is objective and measurable.
An operator may feel subjectively alert while already experiencing elevated impairment risk. The reverse is equally possible: a tired operator may not be critically impaired. This distinction matters because subjective tiredness does not reliably correlate with objective impairment.
Optalert’s Johns Drowsiness Scale (JDS™) addresses this directly. Using eyelid movement analysis, the JDS quantifies impairment risk in real-time, measuring physiological biomarkers rather than relying on indistinct behavioural cues.
The system detects impairment 15 to 30 minutes before a driver reaches a critical threshold. Validated across more than 140 peer-reviewed studies and assessed by Harvard Medical School as commensurate with gold standard laboratory measures, the JDS provides objective, early-stage visibility that no subjective measure can replicate.
This allows operations to identify elevated risk earlier, unlocking opportunities for proactive intervention before impairment reaches a critical level,
What this means for mining leaders and managers
Mining leaders are increasingly expected to demonstrate that drowsiness risk is being actively managed, not simply acknowledged.
Reactive systems offer limited visibility into how impairment develops across a shift. Predictive monitoring offers a fundamentally different capability: the ability to identify elevated risk before an incident occurs.
For operations managing large mobile equipment fleets, the practical benefits are direct:
- Reduce drowsiness-related accidents by >95%.
- Increase on-site production by up to 5%.
- Reduce maintenance and fuel costs by 4.1% and 1.8% respectively.
- Implement drowsiness management programmes you can defend to upper management.
- Optimise roster and operational planning decisions to further reduce risk.
The goal is not to detect drowsiness risk. It is to prevent it.
From reactive response to proactive risk management
Mining operations already rely on real-time visibility to manage equipment health, production efficiency, and environmental hazards. Human impairment should be managed with the same operational discipline.
Optalert’s Eagle Suite gives mining operations objective, real-time impairment data across entire fleets, identifying elevated drowsiness risk 15 to 30 minutes before it becomes critical. The result is a demonstrable shift from reactive incident response to proactive risk prevention.
The operations that will lead the next generation of drowsiness management will not be the ones that respond fastest after visible impairment appears. They will be the operations that identify risk early enough to prevent critical risk altogether.
Contact Optalert to discuss how predictive drowsiness monitoring can reduce impairment-related risk across your operation.
References
[1] ICMM Safety Performance: Benchmarking progress of ICMM company members in 2024 – https://www.icmm.com/en-gb/research/health-safety/benchmarking-2024-safety-data
[2] AAA Foundation for Traffic Safety drowsiness self-assessment study – https://newsroom.aaa.com/2023/03/asleep-at-the-wheel-drivers-unaware-of-how-drowsy-they-really-are/
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