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Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation

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... [1]. Advanced Air Mobility (AAM) builds upon the UAM concept by incorporating use cases not specific to operations in urban environments [2]. A significant economic barrier on the introduction of these concepts is the cost of an onboard human vehicle operator. ...
... Multistate classifiers implemented using machine learning and deep learning techniques. Multi-state prediction using this method can identify non-nominal attentional states at rates >80% [2,18]. ...
... Another method has also been presented [29], using heart rate variability, finger plethysmogram amplitude, and perspiration behavior to assess workload. Other methods should be explored, such as multi-modal classifications using galvanic skin response and pre-processed electroencephalography, or measures of autonomous nervous system responses [2][3][4][5][6] to detect an overloaded operator toward the allocation of functions. ...
... The United States Air Force Research Laboratory has used psychophysiological features to predict functional state during multiple flight tasks, including the Multi-Attribute Task Battery (MATB) and an air traffic control task [Christensen et al. 2012;Wilson and Russell 2003a,b]. Crew state monitoring using multi-modal physiological sensing has also been tested at NASA Langley Research Center in flight simulation cockpits [Harrivel et al. 2016[Harrivel et al. , 2017. Specifically, the crew state monitoring team was able to achieve an average multi-state prediction accuracy of 88.6% using electroencephalography (EEG), galvanic skin response (GSR), and heart rate variability (HRV) with a subject-dependent model. ...
... The MATB is a well-validated tool complete with system monitoring, tracking, resource management, and communications tasks that can be adjusted in frequency and difficulty to simulate high and low workload flight events [Comstock and Arnegard 1992]. Both the PST and the MATB have been used in previous psychophysiological monitoring studies [Das et al. 2017;Harrivel et al. 2016;Saha et al. 2017;Wilson and Russell 2003b]. As mentioned previously, subjective assessments, namely the NASA Task Load Index (TLX), and performance metrics of speed and accuracy from the PST and MATB trials will be used to validate the high and low workload labels. ...
Conference Paper
As next-generation space exploration missions necessitate increasingly autonomous systems, there is a critical need to better detect and anticipate crewmember interactions with these systems. The success of present and future autonomous technology in exploration spaceflight is ultimately dependent upon safe and efficient interaction with the human operator. Optimal interaction is particularly important for surface missions during highly coordinated extravehicular activity (EVA), which consists of high physical and cognitive demands with limited ground support. Crew functional state may be affected by a number of variables including workload, stress, and motivation. Real-time assessments of crew state that do not require a crewmember's time and attention to complete will be especially important to assess operational performance and behavioral health during flight. In response to the need for objective, passive assessment of crew state, the aim of this work is to develop an accurate and precise prediction model of human functional state for surface EVA using multi-modal psychophysiological sensing. The psychophysiological monitoring approach relies on extracting a set of features from physiological signals and using these features to classify an operator's cognitive state. This work aims to compile a non-invasive sensor suite to collect physiological data in real-time. Training data during cognitive and more complex functional tasks will be used to develop a classifier to discriminate high and low cognitive workload crew states. The classifier will then be tested in an operationally relevant EVA simulation to predict cognitive workload over time. Once a crew state is determined, further research into specific countermeasures, such as decision support systems, would be necessary to optimize the automation and improve crew state and operational performance.
... CAST identified distraction resulting from attention-related human performance limiting states (AHPLS) as a contributing factor to loss of airplane state awareness (ASA) in a set of 18 commercial aviation accidents and incidents [2]. The CSM team conducted a series of research studies utilizing a suite of sensors to detect and identify the psychophysiological signatures of suboptimal mental states (channelized attention, diverted attention, startle/surprise, and confirmation bias) while pilots engaged in a state-targeted high-fidelity air traffic flight simulation [3,4,5]. This work demonstrated the ability to produce real-time classification of targeted states using multiple modalities collected with convenient sensor technologies during scenarios designed to induce AHPLS. ...
... In anticipated future flight operations with commercial aircraft and emergent UAM vehicles as described in this paper, the skill level of the human operators is expected to vary greatly, thus underlining the importance of monitoring highly variable human performance. To collect objective data regarding human experience and performance within these new systems, the TUCaNs will include CSM systems [3,4] based on psychophysiological monitoring of the human operators to enable coordinated stimulus presentation and mental state prediction. ...
... These two states are characterized by the disengagement of the executive network, underpinned by the deactivation of the dorsolateral prefrontal cortex (Durantin et al., 2014;Harrivel, Weissman, Noll & Peltier, 2013). Secondly, attentional over-engagement, also referred to attentional tunneling (Wickens, 2005) and "channelized attention" (Harrivel, et al., 2016), is defined as "the allocation of attention to a particular channel of information, diagnostic hypothesis or task goal, for a duration that is longer than optimal, given the expected cost of neglecting events on other channels, failing to consider other hypotheses, or failing to perform other tasks". Some authors postulate that this impaired attentional state results from a disengagement deficit of the orientation network underpinned by the thalamus (LaBerge et al., 1992). ...
... Here the adaptation involves an attention management training approach to complement the usual observations of airline training instructor pilots by informing them, in the training context, of the occurrence of attention-related human performance limiting states (AHPLS) experienced by their trainees. Classifier models are trained to recognize trainee state during simulated flight scenarios based on patterns of the physiological signals measured during benchmark tasks (Harrivel, et al., 2016). Machine learning models' real time determinations of the cognitive states induced by the scenario tasks are displayed as gauges embedded in a mosaic of windows that also displays real time images of the scenario tasks that the trainee is performing (e.g., scene camera, simulator displays, animation of simulator controls), and this mosaic 1 is video recorded . ...
Chapter
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Human operators interacting with machines or computers continually adapt to the needs of the system ideally resulting in optimal performance. In some cases, however, deteriorated performance is an outcome. Adaptation to the situation is a strength expected of the human operator which is often accomplished by the human through self-regulation of mental state. Adaptation is at the core of the human operator’s activity, and research has demonstrated that the implementation of a feedback loop can enhance this natural skill to improve training and human/machine interaction. Biocybernetic adaptation involves a “loop upon a loop,” which may be visualized as a superimposed loop which senses a physiological signal and influences the operator’s task at some point. Biocybernetic adaptation in, for example, physiologically adaptive automation employs the “steering” sense of “cybernetic,” and serves a transitory adaptive purpose – to better serve the human operator by more fully representing their responses to the system. The adaptation process usually makes use of an assessment of transient cognitive state to steer a functional aspect of a system that is external to the operator’s physiology from which the state assessment is derived. Therefore, the objective of this paper is to detail the structure of biocybernetic systems regarding the level of engagement of interest for adaptive systems, their processing pipeline, and the adaptation strategies employed for training purposes, in an effort to pave the way towards machine awareness of human state for self-regulation and improved operational performance.
... The initial results quantified the ability to discriminate between cognitive states as induced by benchmark tasks. 3 In the current study, the same benchmark tasks were used with new pilot participants to train classifier models which are then used to predict the cognitive state of those participants during flight simulation scenarios. The initial focus is on the states of Channelized Attention and Startle/Surprise. ...
... Use of benchmark tasks was modeled after the methods of Hirshfield, et al. 16 The AHPLS to be predicted and the selected benchmark tasks are listed in Table 1 and were described previously. 3 These tasks are used to induce AHPLS under controlled conditions for 6 minutes each, and were chosen for their high likelihood to induce these experiences in isolation and with the full knowledge of the participant (except for the startle task and the high versus low workload condition). Many of these tasks have been employed in previous task-oriented research. ...
Conference Paper
The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled “Training for Attention Management.” To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.
... Based on the insights gained from previous work, we will include eye tracking to record pilots' eye movements, as well as their heart rate (HR), base/resting pulse (BP), and endodermal activity (EDA). The combination of these measures allows the framework to model the pilot's psychophysiological state, which has been indicated by previous work [36]. Furthermore, we will record the aircraft state and the pilot's inputs, which provide the framework with context like the type of situation and reaction of the pilot, specifically during an aircraft failure [37]. ...
Conference Paper
Eye tracking has a longstanding history in aviation research. Amongst others it has been employed to bring pilots back "in the loop", i.e., create a better awareness of the flight situation. Interestingly, there exists only little research in this context that evaluates the application of machine learning algorithms to model pilots' understanding of the aircraft's state and their situation awareness. Machine learning models could be trained to differentiate between normal and abnormal patterns with regard to pilots' eye movements, control inputs, and data from other psychophysiological sensors, such as heart rate or blood pressure. Moreover, when the system recognizes an abnormal pattern, it could provide situation specific assistance to bring pilots back in the loop. This paper discusses when pilots benefit from such a pilot-aware system, and explores the technical and user oriented requirements for implementing this system.
... Furthermore, learning-based classifiers have also been used to detect high and low anxiety in drivers from ECG and accelerometer data (Dobbins and Fairclough, 2018), or to create a virtual driving platform to maximize engagement in people with autism spectrum disorder (Bian et al., 2019). Applications for airplane pilots used classifiers to identify features from EEG and skin response signals that can model the users in scenarios of attention-related human performance limiting states (Harrivel et al., 2016) or to find relationships of cardiovascular features with psychophysiological stress while performing piloting maneuvers (Hanakova et al., 2017). To the best of our knowledge, this is the first project that aims at characterizing psychophysiological responses of police officers on duty for designing biocybernetic loops in VR firearms training. ...
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Crucial elements for police firearms training include mastering very specific psychophysiological responses associated with controlled breathing while shooting. Under high-stress situations, the shooter is affected by responses of the sympathetic nervous system that can impact respiration. This research focuses on how frontal oscillatory brainwaves and cardiovascular responses of trained police officers (N = 10) are affected during a virtual reality (VR) firearms training routine. We present data from an experimental study wherein shooters were interacting in a VR-based training simulator designed to elicit psychophysiological changes under easy, moderate and frustrating difficulties. Outcome measures in this experiment include electroencephalographic and heart rate variability (HRV) parameters, as well as performance metrics from the VR simulator. Results revealed that specific frontal areas of the brain elicited different responses during resting states when compared with active shooting in the VR simulator. Moreover, sympathetic signatures were found in the HRV parameters (both time and frequency) reflecting similar differences. Based on the experimental findings, we propose a psychophysiological model to aid the design of a biocybernetic adaptation layer that creates real-time modulations in simulation difficulty based on targeted physiological responses.
... The general aim of pilot training is to (i) teach the correct understanding and handling of an aircraft, (ii) continuously increase the level of automation as to (iii) enable correct decision-making and task execution even under high pressure. To avoid attentional and procedural errors, such as loss of control in flight and loss of airplane state awareness [12]- [14], pilot actions are generally fixed in rigorous, pre-defined processes. While this approach successfully reduces procedural errors, there is no adaption to individual capabilities in training, putting invested hours on the same level as developed skills. ...
... Finally, other classification methods should be explored, addressing cross-time and cross-task generalizability of the classifier models. Regardless of these potential improvements, we note that the present techniques are ready for investigations in which hemodynamic measures synergistically complement EEG and other psychophysiological measures in multimodal classification schemes [94]. ...
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Brain activity can predict a person’s level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise-thereby increasing such state prediction accuracy-remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% ± 6% versus 72% ± 15%).
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The fundamental approach to improve pilots’ situation awareness (SA) would be to reorganize and restructure the presentation of information to fit pilot's cognitive model on the flight deck. This would facilitate pilots’ perception, understanding, and projection, hence making it easier to find the relevant targets. Sixty pilots (30 B‐737 pilots and 30 B‐777 pilots) participated in this study to investigate pilots’ SA while interacting with digital displays and moving pointed needle displays on cabin pressurization system. The results have shown significant differences on pilots’ perception, understanding, and overall SA between digital display and pointed display on the flight deck. Pilots significantly preferred the digital design cabin pressurization system, which is consistent with the proximity compatibility principle, and the position of the display on the center instrument panel is easily accessible to both pilots and does not require large head movements. There are some recommendations on the cabin pressurization design including the size of outflow valve position indicator, which should be significantly increased to provided saliency of information; color coding should be used on cabin altitude and differential pressure indicator to mark critical cabin altitude; and standard operating procedures shall include cabin altitude and differential pressure reading by pilot monitoring. The final and completed solution to the issues on the cabin pressurization system is to redesign the scattered pointed displays as integrated digital displays to fit the human‐centered principle.
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The ability to distinguish between high and low levels of task engagement in the real world is important for detecting and preventing performance decrements during safety-critical operational tasks. We therefore investigated whether functional Near Infrared Spectroscopy (fNIRS), a portable brain neuroimaging technique, can be used to distinguish between high and low levels of task engagement during the performance of a selective attention task. A group of participants performed the multi-source interference task (MSIT) while we recorded brain activity with fNIRS from two brain regions. One was a key region of the "task-positive" network, which is associated with relatively high levels of task engagement. The second was a key region of the "task-negative" network, which is associated with relatively low levels of task engagement (e.g., resting and not performing a task). Using activity in these regions as inputs to a multivariate pattern classifier, we were able to predict above chance levels whether participants were engaged in performing the MSIT or resting. We were also able to replicate prior findings from functional magnetic resonance imaging (fMRI) indicating that activity in task-positive and task-negative regions is negatively correlated during task performance. Finally, data from a companion fMRI study verified our assumptions about the sources of brain activity in the fNIRS experiment and established an upper bound on classification accuracy in our task. Together, our findings suggest that fNIRS could prove quite useful for monitoring cognitive state in real-world settings.
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Increases in attentional effort are defined as the motivated activation of attentional systems in response to detrimental challenges on attentional performance, such as the presentation of distractors, prolonged time-on-task, changing target stimulus characteristics and stimulus presentation parameters, circadian phase shifts, stress or sickness. Increases in attentional effort are motivated by the expected performance outcome; in the absence of such motivation, attentional performance continues to decline or may cease altogether. The beneficial effects of increased attentional effort are due in part to the activation of top-down mechanisms that act to optimize input detection and processing, thereby stabilizing or recovering attentional performance in response to challenges. Following a description of the psychological construct "attentional effort", evidence is reviewed indicating that increases in the activity of cortical cholinergic inputs represent a major component of the neuronal circuitry mediating increases in attentional effort. A neuronal model describes how error detection and reward loss, indicating declining performance, are integrated with motivational mechanisms on the basis of neuronal circuits between prefrontal/anterior cingulate and mesolimbic regions. The cortical cholinergic input system is activated by projections of mesolimbic structures to the basal forebrain cholinergic system. In prefrontal regions, increases in cholinergic activity are hypothesized to contribute to the activation of the anterior attention system and associated executive functions, particularly the top-down optimization of input processing in sensory regions. Moreover, and influenced in part by prefrontal projections to the basal forebrain, increases in cholinergic activity in sensory and other posterior cortical regions contribute directly to the modification of receptive field properties or the suppression of contextual information and, therefore, to the mediation of top-down effects. The definition of attentional effort as a cognitive incentive, and the description of a neuronal circuitry model that integrates brain systems involved in performance monitoring, the processing of incentives, activation of attention systems and modulation of input functions, suggest that 'attentional effort' represents a viable construct for cognitive neuroscience research.
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It is well known that microgravity results in various physiological alterations, for example, head-ward fluid shifts which can impede physiological adaptation. Other factors that may affect crew operational efficiency include disruption of sleep-wake cycles, high workload, isolation, confinement, stress, and fatigue. From an operational perspective, it is difficult to predict which individuals will be most or least affected in this unique environment given that most astronauts are first-time flyers. During future lunar and Mars missions space crews will include both men and women of multi-national origins, different professional backgrounds, and various states of physical condition. Therefore, new methods or technologies are needed to monitor and predict astronaut performance and health, and to evaluate the effects of various countermeasures on crew during long-duration missions. Herein we describe the development and validation of a new methodology for assessing the deleterious effects of spaceflight on crew health and performance. We reviewed several studies conducted in both laboratory and operational environments with men and women ranging in age between 18 to 50 yr. The studies included the following: soldiers performing command and control functions during mobile operations in enclosed armored vehicles; subjects participating in laboratory tests of an anti-motion sickness medication; subjects exposed to chronic hypergravity aboard a centrifuge; and subject responses to 36-h of sleep deprivation. Physiological measurements, performance metrics, and subjective self-reports were collected in each study. The results demonstrate that multivariate converging indicators provide a significantly more reliable method for assessing environmental effects on performance and health than any single indicator.
Article
We have investigated the quantitative effects of a number of common elements of QRS detection rules using the MIT/BIH arrhythmia database. A previously developed linear and nonlinear filtering scheme was used to provide input to the QRS detector decision section. We used the filtering to preprocess the database. This yielded a set of event vectors produced from QRS complexes and noise. After this preprocessing, we tested different decision rules on the event vectors. This step was carried out at processing speeds up to 100 times faster than real time. The role of the decision rule section is to discriminate the QRS events from the noise events. We started by optimizing a simple decision rule. Then we developed a progressively more complex decision process for QRS detection by adding new detection rules. We implemented and tested a final real-time QRS detection algorithm, using the optimized decision rule process. The resulting QRS detection algorithm has a sensitivity of 99.69 percent and positive predictivity of 99.77 percent when evaluated with the MIT/BIH arrhythmia database.
Improving Engagement Assessment by Model Individualization and Deep Learning
  • F Li
Li, F., "Improving Engagement Assessment by Model Individualization and Deep Learning," Dissertation, Old Dominion University, 2015.
A Systematic Approach for Real-Time Operator Functional State Assessment
  • G Zhang
  • W Wang
  • A Pepe
  • R Xu
Zhang, G., Wang, W., Pepe, A., and Xu, R., "A Systematic Approach for Real-Time Operator Functional State Assessment," Proceedings of the MODSIM World conference, Hampton, VA., Oct.13-15, 2010.
The Multi-Attribute Task Battery II (MATB-II) Software for Human Performance and Workload Research: A User's Guide
  • Y Santiago-Espada
  • R R Myer
  • K A Latorella
  • J R Comstock
Santiago-Espada, Y., Myer, R. R., Latorella, K. A., & Comstock, J. R., "The Multi-Attribute Task Battery II (MATB-II) Software for Human Performance and Workload Research: A User's Guide," NASA, Langley Research Center, Hampton: NASA/TM-2011-217164, L-20031, NF1676L-12800, 2011.