Q: Why is it important to track fatigue levels among employees?
AM: Fatigue Science works proactively around fatigue. We focus on industries in which operators are doing sensitive, dangerous work. The science is clear: when someone fatigued performs complex or dangerous work, there is a high accident rate and productivity remains low. In the transportation and mining industries, for example, a drowsy driver represents a huge risk. Tracking fatigue is essential for preventing accidents and increasing productivity.

Q: What is the SAFTE Fatigue Model?
AM: We predict fatigue through a bio-mathematical model called SAFTE, which has a long scientific validation process. Basically, the model’s inputs are sleep and circadian factors. The model tracks sleep periods, the time of the year and the amount of sunlight at that time of the year, among other factors. The result, labeled ReadiScore, follows the three aspects that we define as fatigue: cognitive performance, which is an individual’s ability to make straightforward decisions, do arithmetic or communicate properly; reaction time; and lapse likelihood, which is the probability of the person having a microsleep.

The ReadiScore is based on a scale of one to 100. The lower the score, the more fatigued a person is. For example, a score of 70 is equal to an 0.08 percent blood-alcohol level in terms of cognitive impairment. This is the predictive part of fatigue science.

Q: How does ReadiScore leverage machine learning (ML) to predict fatigue?
AM: Historically, we tracked sleep data through a wearable, now called the ReadiWatch. However, customers have told us that it is hard to get operators to wear it every single night. This is where ML became an essential part of our journey. Fatigue Science started filling in the gaps when operators forgot or stopped wearing the watches. The goal was to estimate the operators’ sleep and then run it through the SAFTE model to create the ReadiScore.

ML took us to the next step of our journey. Fatigue Science can predict fatigue among operators who have never worn the watch, effectively estimating their sleep and running it through the SAFTE model to produce a precise score. Through telematics and other integrations, we can know when operators were working and when they were resting. For example, knowing when operators had the opportunity to sleep during the past 10 days is highly helpful. Using these inputs and demographics and leveraging the millions of data points collected over the years, Fatigue Science can predict fatigue precisely without the need for wearables.

Q: How does Fatigue Science work to ensure data privacy of clients and operators?
AM: This is a question we get from all customers. As a SaaS company, we comply with all regulations and have all global cybersecurity certifications. We work with Amazon Web Services (AWS) to ensure careful control of data and we are careful to not collect medical data from operators. Regarding sleep data, we do not provide operators’ sleep information to their employers. Operators’ sleep information is 100% personal and private. Employers and supervisors only see the ReadiScore. Fatigue Science works closely with clients to ensure that the data is not used for punitive measures. If operators are not fit for duty, it is not a fatigue issue but a human resources issue.

Q: How does Fatigue Science’s solution adapt to the mining and transportation industries?
AM: Our product is a SaaS and does not require much adaptation. However, there are differences depending on the industry. For example, in the mining sector, we provide supervisors and managers with the ReadiSupervise tool, which gives them instant alerts on operators’ fatigue and tracks the status of the entire crew.

In the transportation industry, dispatchers make similar decisions as supervisors in the mining sector. For this segment, we provide the ReadiDispatch tool, which helps dispatchers to make decisions around who is going to drive and when, in addition to helping them deploy fatigue-mitigation strategies. In both industries, deadlines must be met and operations cannot stop. We make sure that clients are aware of the fatigue status of operators.

RE: Our clients in Mexico and Latin America use our technology to make decisions at the beginning of shifts. They can complete their assessment of workers in a quantitative way, reducing risks and helping them to be more productive and have fewer accidents. Our clients are happy with our solution because they quickly perceive the benefits of predicting fatigue. 

Q: How is Fatigue Science working to expand operations in Mexico and Latin America?
RE: Fatigue Science is growing by using ML and visual intelligence and leveraging all the data collected throughout the years. We are preparing to open an office in Latin America (Mexico). Meanwhile, we already have Spanish-speaking staff to provide technical assistance, sell and implement the solution in the region.

Clients are happy that Fatigue Science has bilingual staff both in Canada and Mexico. As Fatigue Science continues growing its client portfolio, we aim to continue providing the best added-value solution to our customers, which have worked with us for several years. Our technology can save lives. The goal is for operators to go to work and return home safely every day.