AI Case Study
Rio Tinto machinery operators wear SmartCaps to detect dangerous levels of fatique by applying neural networks to brain signals
Rio Tinto has introduced SmartCaps which its truck drivers and other heavy machinery operators must wear to monitor levels of fatigue via electroencephalography (EEG) signals. This provides predictive alerts for when someone is approaching dangerous levels using machine learning neural networks.
Mining And Metals
"The technology has been rolled out primarily with truck drivers and machinery operators, who are at risk from fatigue-related injuries. The SmartCap provides an early warning when
a driver is approaching microsleep. At Rio Tinto, truck drivers
are required to discuss a fatigue management plan with
supervisors if their SmartCaps show they have high levels of
fatigue. Mining companies are using SmartCap data to better
understand the dynamics of fatigue and improve workplace
Health And Safety
"Mining companies in Australia, including Rio Tinto, Anglo
American and Newcrest Mining, are providing field workers
with smart baseball caps (known as SmartCaps) that monitor
their brainwaves to measure fatigue."
"SmartCap uses Universal Fatigue Algorithm based on a data-driven approach. This means that the algorithm is based
on real EEG from a large number of individuals, where the multitude of individual relationships are mapped using machine learning. The core component of the fatigue algorithm is a series of artificial neural networks that are trained to ‘learn’ relationships between the frequency content of an individual’s EEG and a measure of their drowsiness. This learning is achieved by presenting the networks with large numbers of examples of
each drowsiness state, and the corresponding EEG frequency information for numerous participants, and applying mathematical techniques to optimally capture this relationship in a highly non-linear, multidimensional set of equations.
There are two major advantages for taking this learning approach to algorithm development. Firstly, the approach does not require any calibration prior to use. Secondly, the ability for ongoing improvement and refinement. The drowsiness state is determined by independent, non-EEG measures. The most commonly utilised measures in sleep research are the Oxford Sleep Resistance Test (OSLER test), and the Psychomotor Vigilance Test (PVT). Both tests were used to establish the example dataset used to generate the Universal Fatigue Algorithm."