Inside Fatigue Science

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New Research: 3 Mine Sites Reveal Readi Can Predict 12X Higher Chance of Microsleeps

New research released today from Fatigue Science, the leader in predictive fatigue management technology, revealed the striking ability of its Readi platform to predict the likelihood of an operator’s microsleeps before high-risk work begins.
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How Driver Fatigue Affects the 7 Key Fleet Safety Metrics

Fatigue Science, the maker of the predictive fatigue management software suite, Readi, aims to make a difference in the trucking sector by enabling fleets to act on driver fatigue before critical moments occur.
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Featured in International Mining: Readi deployed by Newcrest, using ML to predict fatigue

We are thrilled to be featured in International Mining's latest article, highlighting the deployment of our predictive fatigue management technology, Readi, by Newcrest Mining at their Lihir gold mine in Papua New Guinea.
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US DoT Study: Readi’s SAFTE can predict 7.3x accident cost from fatigue

A 2013 study by the US Department of Transportation (US DoT) is receiving newfound attention, due to the recent release of ReadiML, the Machine Learning software from Fatigue Science that operationalizes a scientific fatigue prediction model for daily use by transportation fleets.
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The True Cost of Driver Fatigue for Your Trucking Fleet

Fatigue is a well-known hazard in the trucking industry, with truck driver fatigue being a leading causal factor of numerous roadway accidents every year.
Machine Learning predicts truck accidents via ELD data
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Machine Learning breakthrough predicts truck accidents and fatigue from ELD data, improving fleet safety records

Recent advances in Machine Learning are enabling trucking fleets to predict each driver’s probability of getting in a fatigue-related accident on any upcoming shift—before the driver gets behind the wheel.
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Predicting fatigue with machine learning

Enjoy this great article about supporting safety with technology featuring Readi FMIS published by Paul Adair, WDC Staff Writer for Women Driving Change magazine.
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Newcrest’s Lihir mine deploys Readi site-wide for predictive fatigue management

Fatigue Science is proud to announce that Newcrest Mining Limited has deployed its Readi predictive fatigue management technology site-wide at its Lihir mine site in Papua New Guinea.
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Fatigue Science Wins Green Cross Safety Innovation Award

The National Safety Council announced Fatigue Science wins its prestigious Green Cross Award for Safety Innovation.
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Glencore Lomas Bayas implements Readi FMIS

Glencore's Compañía Minera Lomas Bayas (CMLB), a major producer of low-grade oxide copper ore, announced the implementation of Readi FMIS for predictive fatigue management throughout its site located in the Antofagasta Region.
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National Safety Council names Fatigue Science a Finalist in prestigious Green Cross Award for Safety Innovation

Fatigue Science was recognized for pioneering a new best practice for safety in mining and heavy industry.
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Fatigue Science launches Machine Learning engine providing world’s first workforce fatigue predictions without wearables

Fatigue Science is pleased to announce the commercial launch of a breakthrough technology advance in its fatigue management platform. For the first time ever, customers can now obtain validated, personalized fatigue predictions for operators in their workforce, without requiring the use of wearables.
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Supervisor App Upgrade: Offline Mode and Hotspots View for shift supervisors

Fatigue Science announces Offline Mode and Hotspots View for shift supervisors, enabling proactive fatigue intervention in underground and remote environments.
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New data: Shift schedules affect the sleep quality of “night owls” and “morning birds” differently, impacting safety and productivity on the job

Fatigue Science announces new findings on the relationship between sleep quality and the rigorous schedules faced by shift workers.
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Mine sites achieve $6M benefit using predictive fatigue tech

Discover how 2 mines each achieved a $6M annual benefit in productivity and safety improvements using Readi FMIS.