Background of the Science behind SmartCap

Prior to the SmartCap technology development, gauging impairment related to fatigue included the measurement of behavioural symptoms such as eye behaviour, gaze direction, micro-corrections in steering and throttle use, and heart rate variability. The measure most commonly used in fatigue monitoring technologies is the percentage of eye closure, or PERCLOS. While studies have shown correlation between PERCLOS and impairment, approaches using this measure are susceptible to changes in eye behaviour unrelated to changes in alertness. Examples include situations of glare, insufficient lighting, dust and changes in humidity. As such, practical implementations usually suffer from higher rates of false alarms and missed instances of impairment.

The underlying measurement behind the SmartCap levels is brain activity. Often referred to as EEG (or electroencephalogram), brain activity has been the golden standard in sleep and fatigue science for over 30 years. Being a more direct physiological measure, this allows the SmartCap technology to provide greater accuracy by avoiding erroneous measurements related to the external environment.

This was made possible by the innovation of the SmartCap Technologies patented EEG technology developed for the SmartCap. This world-first technology delivers clinical-grade EEG using dry electrodes, meaning no scalp preparation or gels are required. The advanced electronics are able to be concealed in a range of headwear designs, making SmartCap headwear comfortable to wear, and readily customisable with corporate colours and logos.

The measurement of EEG through practical, wearable technology solves half the problem of accurate fatigue monitoring in a working environment. What remains is the universal mapping of EEG information to deliver a useful measurement of fatigue.

While the analysis of EEG is a well-established science, researchers have always found that expert-developed rules to interpret brain activity tend to be effective for a majority and not the entire population. This is a result of natural person-to-person variation based on different physiology. Examples of variations identified include age and gender. Such variation means that a rule-based approach to mapping EEG to a measurement of fatigue would require an expert rule for each physiology, and to know which rule should apply to each person. This is clearly impractical.

In order to produce a practical tool for fatigue monitoring, SmartCap Technologies developed the 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 (often referred to as Artificial Intelligence) techniques. EdanSafe are experts in this field, and to date the SmartCap Universal Fatigue Algorithm is the only data-driven mapping of EEG to a measure of fatigue in commercial use.

Testing the performance of the Universal Fatigue Algorithm has been at the heart of the development process over the last decade. An unseen-blinded experiment approach is always used, meaning that that brain activity information presented for testing has not been used in the training of the algorithm and that the independent measure of drowsiness is viewed and compared after the calculations are completed.

This approach is useful for developers, however the true test of an algorithm is to subject it to independent assessment. The SmartCap Universal Fatigue Algorithm was assessed independently by the Monash University Accident Research Centre. This represented the world’s first independent, expert assessment of a fatigue monitoring technology. All data used for the assessment was and remains quarantined by the scientists involved in the study, and has never been provided to EdanSafe.

The study involved a number of people (subjects) that volunteered to participate. Each subject participated in a number of tests to determine their ability to resist sleep while also wearing a SmartCap. No SmartCap data was provided to the participants during the experiment – measurements were recorded for later comparison. It is important to note that in the field of sleep and fatigue science, the laboratory setting is considered the most relevant for expert assessment, since the ‘controlled’ environment ensures that the performance (or lack thereof) of the test device (SmartCap) is accurately assessed.

Once all data was collected, the SmartCap Fatigue Levels were compared with the expert assessment of fatigue impairment from each of the tests. The independent review found that the SmartCap Universal Fatigue Algorithm was 94.7% accurate in identifying instances of significant impairment related to fatigue. This was based on comparison with OSLER test results, which is a well-accepted evaluation approach for identifying impairment associated with fatigue.

There are a number of specialised techniques we have applied during the training and testing process to ensure that these equations cater for as much person-to-person variability as possible. Our experience is that the more data from a wider range of participants is used, the more universal the result. This also provides the advantage that as more laboratory data is collected, the SmartCap Universal Fatigue Algorithm can be made to be more accurate and discerning over time.

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