The objective of these studies was to meet the following five criteria: 1) ensure maximal stability and inter-individual generalizability by using a large sample size, 2) accommodate individual variability, 3) be applicable across tasks (task generalizability), 4) be computationally accurate and efficient for use in a portable un-tethered hardware application, and 5) provide a hardware platform to apply the algorithm in the field. The current study utilized data from a large sample size (n=160) to build an algorithm that begins to address generalizability. This data set is substantially larger than the largest sample size in previously published drowsiness algorithms (n=30)(Subasi 2005
). Individual variability was accommodated through individualization of the model centroids based on a 15 minute set of 3 “ID” tasks (EO, EC, and the first 5 min of the 3CVT). This individualization method makes cross-validation mathematically invalid; thus, generalizability and validation had to be assessed in an alternative manner. We were able to demonstrate that the algorithm’s designation of drowsiness (SO + DIS probability) tracked errors across multiple laboratory and simulation tasks, with significant correlation to error rates over time when subjects were sleep deprived. In order to accommodate computational efficiency, the algorithm was limited to data from Fz-POz and Cz-POz (as opposed to algorithms that required up to 32 channels). Additionally, as eye blink and muscle movement are inherently present in all EEG acquisition, the current system is able to identify a range of subtle and large artifacts (including eyeblinks) using EEG alone, removing the need to use EOG. The algorithms developed for EEG artifact decontamination can identify and remove contaminated signals automatically throughout acquisition, whereas past studies have relied upon offline post-hoc analysis (Gevins and et al. 1977
; Santamaria and et al. 1987
; Coenen 1995
; Horne and Reyner 1995
; Makeig and Jung 1996
; Huang, Jung et al. 2005
Error prediction is the only acceptable outcome of a drowsiness detection application in the field to avoid both fatal and non-fatal errors. While the current method provides an excellent foundation, these data indicate that the current solution must address several shortcomings. First, the current method requires an acquisition PC (laptop, palm pilot, etc) to be within 30 ft, however, the algorithm can be programmed on an ASCII chip, allowing for onboard data collection with the headset alone for up to 12 hr. This step has been evaluated as a “proof of concept,” and the current algorithm could be acceptable in such an application. Second, the current algorithm, while quite effective under sleep deprived conditions, was less effective when subjects were rested, leading to a great potential for false alarms. Such false alarms would highly diminish productivity, reduce adoption, and reduce compliance with policies based on drowsiness assessment. Third, though the current solution was found to be effective across multiple tasks, these tasks were all tested under laboratory conditions. For example, the simulated driving did not include scenarios reminiscent of rush hour type traffic, where little movement occurs for long periods of time, but the density and potential for errors is greater. There is no data available to compare how the drowsiness algorithm might perform under either simulated “rush hour” traffic or actual driving conditions that shift from low density to high density depending upon the path chosen, time constraints, skill level of driver, and other variables. If an algorithm is to be adopted, such a comparison would be required prior to adoption. Finally, in addition to the discrepancy in accuracy between tracking errors under rested conditions and sleep deprived conditions, the current algorithm does not predict performance decrements. These shortcomings will be addressed in future development projects that will include field studies to determine the applicability of the algorithm outside of the laboratory.
The algorithm was applied in a recent field study using professional truck drivers driving on a 37 kilometer closed driving track located in rural Germany. This study presented a very limited number of stimuli (18 in a 7 hr protocol) that drivers were instructed to identify as they drove 6 laps around a 37 km track at approximately 40 km/hr. These data found that fewer errors occurred during the morning session, when the drivers were fully rested, and the few errors that did occur were not related to the drowsiness metric. On the other hand, a relationship began to appear in the afternoon, when errors were associated with elevated drowsiness levels in the final two laps (although no significant correlation was found) (Bingham and Kincses 2008
). These data are consistent with the data presented herein, whereby the drowsiness metric does not track errors for fully rested persons, and only begins to correlate as participants grow fatigued near the end of the day. These data support the need for further development to avoid false alarms and misses in the field, as well as the need for further field studies.
If any drowsiness algorithm is to prove useful in preventing accidents, it must meet the criterion discussed above. This is a shared shortcoming with most other drowsiness detection algorithms reported thus far (with the exception being the actigraph solution proposed by researchers at Walter Reed) (Balkin, Belenky et al. 2002
). A predictive solution is under investigation at this time. Any such solution must have a limited false alarm rate, as well as a very low miss rate, to ensure that it is useful in improving public health and safety environments.
One potential explanation for the failure of the current algorithm to correlate with performance under rested conditions is individual variability. Individual variability occurs in performance, with some persons learning a task more quickly and accurately than others. While the current strategy of using an individual’s coefficient matrix to build an individualized model may work for many subjects, further individualization may be required. For instance, the current solution relies on a single underlying model, while recent studies indicate that three or more “phenotypes” may exist, and thus a general model for each might be more effective (Doran, Van Dongen et al. 2001
; Van Dongen and Dinges 2001
; Rajaraman, Gribok et al. 2008
; King, Belenky et al. 2009
). Our data supports this hypothesis as well. In study 2 (n=25), we found that some individuals (n=4, 16%) were impervious to sleep deprivation and performed within normal parameters even after 40+ hours without sleep. On the other hand, we also indentified a small number of individuals (n=6, 24%) that were already significantly impaired when only 12–18 hours have passed since they last slept (i.e. at 7–11 pm in study 2). Thus, a single general model may not be sufficient, even with individualization. The underlying model may vary based on vulnerability to sleep deprivation or other variables (such as age or gender). These concerns will be further evaluated in future development projects.
In addition, the current algorithm relies on the assumption that a person’s basal EEG and performance in the rested condition remains stable over a period of time. We were able to evaluate stability over a 24 hr period: from the screening to the rested days in study 1. Further assessment is required, however, to determine if fully rested EEG changes over a period of days, weeks, or months.
The current data are only the first step in developing a drowsiness detection system that can be implemented to prevent industrial and/or vehicular errors associated with drowsiness, and additional work is required to have a fully filed validated and deployable algorithm. At this time the algorithm has shown to be robust in tracking inaccuracy and errors associated with sleep loss across multiple tasks (3CVT, IR, IIR, and DRIVE). In order to provide a useful tool for broad adoption in vehicular and/or industrial settings, the algorithm must further be developed in order to identify states that predict the onset of increased errors, with enough lead time to allow for an appropriate intervention to occur, to reduce false alarms associated with rested conditions, and ensure stability of the algorithm over time. Current investigations are underway to develop such a predictive algorithm. While the current algorithm has not demonstrated this level of utility, it has proven useful in multiple applications, some with an emphasis on drowsiness, others with no such emphasis. The algorithm has been utilized in altering information flow to increase productivity without overloading the user (Berka, Levendowski et al. 2004
; Berka, Levendowski et al. 2007
; Berka 2008
). The algorithm has proven useful in other research environments as well, particularly in field applications (Stevens, Galloway et al. 2006
; Berka 2007
; Stevens, Galloway et al. 2007
; Bingham and Kincses 2008
The demands of the global society for round-the-clock operations are likely to continue to increase the incidence of sleep deprivation worldwide. Although the deleterious effects of fatigue as a result of sleep deprivation or untreated sleep disorders on public safety are as well documented as those of alcohol intoxication (Jones, Dorrian et al. 2006
; Howard, Jackson et al. 2007
), these findings have not yet been operationalized as policy or legal initiatives. One reason frequently cited for the lack of policy or regulation of driver drowsiness is the ongoing debate over whether an accurate and reliable assay for drowsiness can be developed for routine use in field applications. An equally important practical concern is to determine what level of fatigue causes performance impairment sufficient to result in motor vehicle and other accidents. Alternatively, when implemented in a closed-loop alarm feedback system, a drowsiness detection device can empower individuals to better monitor their levels of fatigue and make informed decisions regarding their level of risk to themselves and others. The system described herein (deemed the “B-Alert” method) holds potential to address these unmet needs for objective quantification of drowsiness to assist healthcare providers and other officials tasked with ensuring public safety. Utilization of the B-Alert system/method in industrial risk mitigation also has great potential. It is task independent, i.e., drowsiness detection does not rely on task specific metrics, and thus the system can potentially be applied in many different real-world environments (Stevens, Galloway et al. 2007
; Stevens, Galloway et al. 2007
; Berka 2008
- In this study a drowsiness-alertness continuum algorithm was developed
- The algorithm was validated across multiple individuals (n=160) and multiple cognitive and driving tasks
- The drowsiness metric was able to track errors when participants were sleep deprived (but not when rested)
- Further investigation is required to determine field applicability