In industries like mining, construction, and transportation, worker fatigue is a critical safety risk that often goes unseen until an incident occurs. Long shifts, overnight schedules, and physically demanding work can lead to reduced alertness, slow reaction times, and dangerous microsleeps. Traditional approaches – from hours-of-service rules to in-cab camera alarms – tend to be reactive, alerting you only once fatigue is already a problem. To truly stay ahead of fatigue, a proactive solution is needed.
That’s where Fatigue Science’s Readi platform comes in. At the core of this fatigue risk management system is the ReadiScore, a predictive metric that acts as an early warning for fatigue-related impairment.
What is the ReadiScore, how does it work in the Readi platform, and why is it becoming a game-changer for safety and operations leaders in heavy industry? Keep reading to find out
ReadiScore is essentially a personal fatigue alertness score on a scale from 0 to 100. A higher ReadiScore means a worker is well-rested and alert, while a lower score indicates elevated fatigue risk.
Studies have shown a direct relationship between ReadiScore and real-world performance and safety: as the score drops, reaction times slow and the likelihood of accidents increases.
ReadiScore gives organizations a quantifiable, easy-to-understand measure of fatigue so that they can identify at-risk operators before a microsleep or mistake leads to an incident.
ReadiScore fatigue profile for a worker over one day. In the afternoon, the operator’s ReadiScore was 84 (yellow), indicating only mild fatigue (~18% slower reaction time than a fully rested state). By 12:40 am, the ReadiScore had dropped to 70 (red), corresponding to a 43% slower reaction time. The ReadiScore curve helps both workers and supervisors see when alertness is projected to dip into the danger zone, so they can intervene (e.g. take a break or swap duties) before an accident is likely to occur.
ReadiScore is a key output of Fatigue Science’s Readi fatigue management platform, which combines wearable technology (the ReadiWatch) with advanced machine learning and a biomathematical fatigue model.
Here’s how it works step by step:
Data Collection (Sleep & Work Inputs): The process begins by gathering data about an employee’s sleep and work schedule. This can be done in multiple ways depending on your operations. If the worker wears a Fatigue Science ReadiWatch (a wrist-worn sleep tracker), the device will automatically record their sleep duration and quality each day.
The platform also integrates with operational systems – for example, Time & Attendance logs or Electronic Logging Devices (ELDs) – to understand each person’s work hours and available rest periods. Additionally, a one-time sleep questionnaire (intake survey) and basic profile (e.g. demographics) may be used to personalize the system for each individual. These inputs establish the context of when a person could sleep and some personal factors that might affect their fatigue.
Machine Learning Sleep Prediction: All the collected data feeds into Readi’s AI-powered engine to produce a personalized sleep history for each worker. If a worker wears the ReadiWatch consistently, the platform uses their actual recorded sleep data as the primary input. But if a worker doesn’t wear the watch (or forgets it on a given night), the system doesn’t go blind – instead, it leverages an extensive machine learning model to estimate the person’s sleep based on the other available data.
This Sleep AI, trained on a massive anonymized dataset of over 4–6 million nights of sleep from shift workers around the world, can predict an individual’s likely sleep quantity, quality, and timing even without a wearable. It does this by finding patterns from similar workers’ schedules and demographic info to infer how much the person probably slept during their off-duty periods. In essence, Readi builds a rolling 10-day sleep history for each employee before each shift – whether by using actual ReadiWatch data or by “filling in the blanks” with machine learning predictions when needed. (The system needs at least about three days of sleep data to start generating a reliable ReadiScore, which can come from either watch data or AI predictions.)
Fatigue Modeling & ReadiScore Generation: Once the platform has a profile of an individual’s recent sleep, that information is run through a scientifically validated fatigue model to compute their ReadiScore. The model at work here is the SAFTE™ Biomathematical Fatigue Model – developed by the U.S. Army and validated by the U.S. Department of Transportation and Federal Aviation Administration.
SAFTE takes into account the timing and amount of sleep (or lack thereof) over the past several days and applies circadian science to predict alertness levels. The output is a predicted effectiveness score (0–100) for each hour of the next period of wakefulness, which Fatigue Science presents as the ReadiScore. In practical terms, ReadiScore produces an hour-by-hour fatigue forecast for up to 18 hours into the future. That typically covers the duration of a work shift and any critical periods before or after the shift (such as commute times or overtime) where fatigue could affect safety.
The Readi platform makes this data accessible and actionable: supervisors and managers can see each worker’s predicted ReadiScore curve for the upcoming shift, and workers themselves can check their own score via a mobile app or on the ReadiWatch. The score updates continuously as new sleep data comes in or as time passes, providing a real-time fatigue risk indicator throughout the day.
The ReadiWatch is an optional rugged smartwatch that extends the Readi platform to the field. It automatically tracks a worker’s sleep and displays their fatigue forecast (ReadiScore) right on their wrist. In the example above, the ReadiWatch (shows a wearer slept 6h 52m last night, and the ReadiScore forecast (center) indicates they are 92 now (fully alert), but will likely drop to 80 by 7:05 pm and 70 by 1:40 am. By syncing regularly, the watch ensures ReadiScore calculations are based on high-quality actual sleep data rather than estimates. It can even deliver on-wrist alerts (via vibrations or notifications) when an operator is about to enter a high-fatigue state. Designed for industrial environments (waterproof, durable, and easy to use), the ReadiWatch makes it seamless to monitor fatigue on the front lines – whether your employees are operating a haul truck, a crane on a construction site, or a long-haul vehicle.
Putting it all together, ReadiScore distills complex sleep science into a simple number and graph that tell you who is at risk of fatigue and when during their shift that risk will peak.
Managers access this information through dashboards and mobile alerts (the ReadiSupervise app), allowing them to proactively reassign tasks or schedule a break for a worker predicted to be dangerously fatigued.
Meanwhile, operators can use their personal ReadiScores as a wellness tool – a reminder to prioritize sleep and an objective measure to self-assess fatigue before doing high-risk work. The entire system respects privacy by keeping personal sleep details confidential; it only surfaces the ReadiScore risk level to stakeholders.
Fatigue Science’s ReadiScore isn’t the only fatigue metric out there, but it has some distinct advantages that set it apart from traditional approaches and other fatigue risk tools.
Here are a few key differentiators that matter for high-risk, 24/7 operations:
ReadiScore leverages a trailing 10-day window of sleep information to predict today’s fatigue risk. By accounting for sleep debt accumulation and recovery over more than just last night, it captures the full picture of a worker’s fatigue. This is far more comprehensive than “fit for duty” tests that only consider a moment in time or systems that reset after one good night’s sleep.
In short, ReadiScore knows if you only got 4–5 hours of sleep each night all week, even if you slept 8 hours last night – and it reflects that cumulative fatigue in your score.
Fatigue is personal – the same schedule can affect two people differently. ReadiScore is individualized for each worker, using their own sleep pattern (or personalized sleep estimate) rather than generic averages. This means each person gets a fatigue forecast tailored to their biology and recent behavior.
Moreover, your organization can define what “fatigued” means for your operation by setting custom threshold levels for ReadiScore alerts. For example, you might configure the system to flag any worker falling below a ReadiScore of 75 during a shift, or to require additional verification if someone’s score is under 70. This flexibility allows companies to align the fatigue risk alerts with their specific safety policies and risk tolerance. It’s not a one-size-fits-all number – it’s your fatigue risk dashboard.
One of the biggest advantages of ReadiScore is its predictive horizon. Thanks to the SAFTE model, ReadiScore produces a forecast up to 18 hours into the future. That means you’re not just seeing a worker’s current state, but you can also see if and when they will likely become fatigued before their shift even starts, through the end of the shift, and into any expected overtime or commute home.
This 18-hour predictive window is critical – it lets supervisors take preventive action (such as rescheduling tasks, planning a relief operator, or suggesting a power nap) ahead of time. Contrast this with reactive systems like fatigue cameras that only alert during a microsleep, or periodic alertness tests that give a single data point; ReadiScore provides a continuous fatigue curve so you can manage risk dynamically throughout the dayfatiguescience.com. It’s a proactive approach rather than a reactive one.
Under the hood, Readi’s predictions are powered by a robust AI engine that has been trained on an unprecedented dataset of human sleep in real-work conditions. Fatigue Science has collected over 7 million hours of sleep data (and counting) from workers using its wearables across industries. This trove of real-world data fuels the machine learning model that estimates sleep when a wearable isn’t used, and continuously improves the accuracy of fatigue predictions.
The result is a system that becomes smarter and more precise as more data is accumulated. This AI-driven approach gives ReadiScore a level of validation and confidence that point-in-time tests or simpler biomathematical models (without machine learning) can’t match. It’s like having a fatigue prediction engine that has “seen” millions of shift scenarios before and knows what fatigue outcomes to expect.
ReadiScore isn’t a black box – it’s built on decades of sleep science and has been validated in both laboratories and the field. The underlying SAFTE™ model was developed through 25 years of U.S. Army research and has been vetted by the U.S. Department of Transportation, FAA, and other institutions.
Overall, the fatigue predictions generated by Readi (SAFTE + the ML engine) have shown about 88% accuracy compared to a wearable in validation studies. Perhaps more importantly, real-world deployments have proven that ReadiScore correlates strongly with safety outcomes.
Multiple independent studies and customer results have linked low ReadiScores with higher incidents of microsleeps, accidents, and safety infractions. For example, the U.S. DOT published a study showing a 7.3× higher accident cost in rail operations when operators’ ReadiScores indicated high fatigue. Fatigue Science clients in trucking have observed 8.5× higher rates of harsh braking and 4× higher speeding events when drivers’ ReadiScores were low. And in mining, a recent analysis found that drivers were 12 times more likely to experience microsleep events when their ReadiScore was in a fatigued state compared to when they were fully rested. All of this evidence gives safety managers confidence that the ReadiScore isn’t just a number – it’s a meaningful indicator of risk that has been scientifically validated and proven in practice.
Every operation has different workflows and technology in place, and ReadiScore was designed to fit in seamlessly. Whether your crew is wearing ReadiWatch devices or not, the Readi platform can ingest data from multiple sources – including roster schedules, time/attendance systems, payroll or HR systems, and ELD or telematics data from fleet vehicles. This means the fatigue predictions can be generated in virtually any scenario: 24/7 mining crews, long-haul truck drivers, construction teams on rotating shifts, etc. If a worker isn’t wearing the watch, ReadiScore still works by using their schedule and work logs, supplemented by a quick sleep survey or historical sleep patterns to fill the gaps.
The platform’s API also enables custom integrations, so you can pipe ReadiScore data into your existing dashboards or control-room software if needed. In short, ReadiScore meets you where you are – it augments your current safety processes without requiring a complete overhaul. This flexibility is crucial in heavy industries, where connectivity can be spotty and not every worker will adopt new tech overnight. Readi’s system even supports offline data collection (caching data until a connection is available) for remote sites like underground mines or distant project sites, so fatigue monitoring isn’t disrupted by lack of internet.
ReadiScore and the Readi fatigue management platform have already been adopted in a range of heavy industries – from mining operations to trucking fleets – with impressive results. Below are a few examples of how this technology is making a difference on the ground:
Fatigue Science’s solutions have been deployed at numerous mine sites worldwide to improve shift safety. In one case study, a large Central American copper mine implemented Readi alongside a traditional fatigue camera system (Caterpillar DSS). The result was a 50% reduction in fatigue-related alarm events from the in-cab cameras, thanks to Readi’s ability to predict and mitigate extreme fatigue before operators ever reached that point. By alerting supervisors hours in advance that a haul truck driver would be dangerously fatigued mid-shift, the mine could adjust schedules or provide a controlled rest break, thereby preventing many fatigue alarms (and potentially fatigue-related microsleeps) from happening at all.
Construction projects and other field-based operations face similar fatigue challenges, especially when running 24/7 or late-night shifts to meet deadlines. While specific case studies in construction are emerging, the same principles apply: ReadiScore can use work schedules and optional wearable data from construction crews to forecast fatigue risk on the job site.
For example, if a team is working a 10-night stretch building critical infrastructure, the site supervisor can review each worker’s ReadiScores before a shift and know who might be at high risk by hour 8 or 10 of that night. This enables proactive reallocation of tasks (perhaps assigning a well-rested worker to the most critical safety-sensitive job, while giving a more fatigued worker a lighter duty or an earlier quit time).
Given the rugged nature of the ReadiWatch, it can be worn by heavy equipment operators and field engineers without worry about damage, providing live fatigue alerts even amid construction noise and commotion. Although detailed construction industry data is still growing, it’s clear that any operation with extended or irregular hours stands to benefit from predictive fatigue management. In industries like oil & gas and utilities (which share a lot in common with construction in terms of shift work and remote locations), we’re seeing a shift from reactive fatigue measures to predictive ones – and Readi is at the forefront of that shift.
Driver fatigue is a well-known hazard in transportation, and ReadiScore has been making inroads in this sector by enabling data-driven fatigue prevention for fleets. Trucking companies can integrate Readi with their ELD data and dispatch systems to get a fatigue risk forecast for each driver before they hit the road.
In fact, with recent machine learning advances, a fleet can even predict each driver’s probability of having a fatigue-related accident on an upcoming shift – before the driver gets behind the wheel.
By flagging high-risk trips in advance, dispatchers can make informed decisions, such as adjusting routes, scheduling an alternate rested driver, or enforcing a longer rest period to avert a potential accident.
This predictive approach goes beyond just complying with Hours-of-Service rules; it adds a layer of safety by recognizing that even within legal driving hours, a driver can be dangerously fatigued. As an example of effectiveness, one analysis found that when ReadiScore indicated high fatigue, drivers experienced dramatically more incidents: up to 14× more microsleeps and significantly more speeding and harsh braking events.
Conversely, keeping drivers in a safe ReadiScore range can meaningfully improve overall fleet safety metrics (and even reduce insurance costs over time). Transportation companies operating in remote regions have also embraced Readi – one company in Papua New Guinea integrated Readi into its daily pre-start process for 24/7 operations to ensure drivers were safe to launch on long routes. The ability to customize fatigue thresholds has been valuable in trucking, where a dispatcher might only get an alert if a driver is predicted to drop below, say, 80 (a tighter threshold) given the critical nature of their routes. By using ReadiScore proactively, fleets are not only avoiding accidents but also improving productivity (fewer unexpected stoppages or driver swaps) and optimizing their scheduling with fatigue in mind.
Overall, ReadiScore’s versatility means it can be applied anywhere fatigue poses a risk. Whether it’s an open-pit mine, an underground tunneling project, a cross-country trucking operation, or even an elite sports training program, the core concept is the same: better sleep data and predictive analytics lead to better fatigue risk management. Organizations that have adopted Readi report tangible improvements in safety and performance.
For instance, one mining company reduced fatigue-related incidents by 30% after using Readi insights to optimize their shift schedule and staffing. Another operation saw a 13% decrease in total lost-time injuries after implementing Readi as part of their safety program. These are significant gains for any director in safety or operations – they mean fewer injuries, less downtime, and a healthier, more alert workforce.
Fatigue may be an age-old problem in heavy industries, but with tools like ReadiScore, we finally have a modern, data-driven way to tackle it. By providing clear visibility into who is at risk of fatigue before an accident happens, ReadiScore empowers leaders in health, safety, and operations to make proactive decisions that save lives and improve productivity. The system’s proven results – from cutting fatigue alarms in half to achieving double-digit reductions in incidents – show that predictive fatigue management isn’t just a theoretical concept, but a practical solution being used by companies at the forefront of safety innovation.
Reach out to us to schedule a demo of the Readi platform, see the insights for yourself, and discuss how a predictive fatigue risk management approach could fit into your safety program. With ReadiScore, you can move beyond reactive fatigue mitigation and start preventing accidents before they happen – keeping your crews safer, your projects on schedule, and your mind at ease about the road ahead.