The SmartCap is the world’s only independently validated alertness monitoring tool, made possible by its use of EEG – the gold standard in sleep science – and sophisticated algorithms that account for individual variations. Use the links below to learn about the science of SmartCap, and to get the independent validation reports.
A number of confirmation steps are incorporated into the SmartCap software to ensure the highest level of accuracy possible. Before a fatigue level can be reported, a minimum of 17 seconds of EEG information must be analysed. Within this window of time, 13 separate fatigue calculations have been performed. A measure is only reported if the same measure is calculated in seven consecutive instances. This reported measure is known as the ‘confirmed fatigue result’. If no confirmed fatigue result is achieved within 60 seconds, the reported output is “fatigue unknown”.
Even more confirmation is required when significant impairment related to fatigue is suspected. Once significant impairment is suspected, the SmartCap system shows a level 3+. Calculations continue, with new levels being determined at more frequent intervals. If strict criteria are met, the SmartCap system will confirm its highest level; a level 4.
The core of SmartCap’s Universal Fatigue Algorithm
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.
EEG analysis usually focuses the frequency content of the neural activity. This is also true for the EEG processing included in the SmartCap Processor Cards.
The sensors in the SmartCap headwear incorporate electronics that apply filtering (low pass) of the signals, so that any signals above 40Hz are significantly diminished. When converted to digital values in the processing card under the brim, we sample at 1280Hz, and then convert this to 256Hz – this allows us to ensure that there is no “aliasing” of high frequency noise in the frequency range of interest.
Once we have the 256Hz signals, we calculate the frequency spectrum of the signal over a 5-second window, which is recalculated each second. The frequency spectrum calculated is from 0-64Hz.
Using the entire frequency spectrum is a plausible approach, however if certain frequencies do not provide information related to an individual’s drowsiness, the accuracy would vary significantly from person to person. As such, selective frequency analysis is key to the SmartCap’s accuracy. Also, input data is scaled in a way to effectively make it non-dimensional, allowing person-to-person variations in signal strength and frequency content.
After hundreds of combinations of input structure and scaling were tested, our research found the most suitable to be an input of 1 to 13Hz, in 1Hz increments, scaled by the total signal power in the range of 13-30Hz. This was not by design, but by an extensive trial and error process.
Coincidentally, this result is an input that captures the well-known delta, theta and alpha waves, presented in a finer resolution, and scaled by the power of the beta waves. This could be interpreted as effectively a ratio of an individual’s drowsiness and wakefulness.
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.