System and Method for Training of State-Classifiers

Method and systems are disclosed for training state-classifiers for classification of cognitive state. A set of multimodal signals indicating physiological responses of an operator are sampled over a time period. A depiction of operation by the operator during the time period is displayed. In response to user input selecting a cognitive state for a portion of the time period, the one or more state-classifiers are trained. In training the state-classifiers, the set of multimodal signals sampled in the portion of the time period are used as input to the one or more state-classifiers and the selected one of the set of cognitive states is used as a target result to be indicated by the one or more state-classifiers

Human Performance Contributions to Safety in Commercial Aviation

In the commercial aviation domain, large volumes of data are collected and analyzed on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that contribute to routine successful outcomes, safety management and system design decisions are based on a small sample of non- representative safety data. Analysis of aviation accident data suggests that human error is implicated in up to 80% of accidents, which has been used to justify future visions for aviation in which the roles of human operators are greatly diminished or eliminated in the interest of creating a safer aviation system. However, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant and largely unrecognized risk when making policy decisions about human roles and responsibilities. Opportunities exist to leverage the vast amount of data that has already been collected, or could be easily obtained, to increase our understanding of human contributions to things going right in commercial aviation. The principal focus of this assessment was to identify current gaps and explore methods for identifying human success data generated by the aviation system, from personnel and within the supporting infrastructure

Evaluating the Effects of Dimensionality in Advanced Avionic Display Concepts for Synthetic Vision Systems

Synthetic vision systems provide an in-cockpit view of terrain and other hazards via a computer-generated display representation. Two experiments examined several display concepts for synthetic vision and evaluated how such displays modulate pilot performance. Experiment 1 (24 general aviation pilots) compared three navigational display (ND) concepts: 2D coplanar, 3D, and split-screen. Experiment 2 (12 commercial airline pilots) evaluated baseline 'blue sky/brown ground' or synthetic vision-enabled primary flight displays (PFDs) and three ND concepts: 2D coplanar with and without synthetic vision and a dynamic multi-mode rotatable exocentric format. In general, the results pointed to an overall advantage for a split-screen format, whether it be stand-alone (Experiment 1) or available via rotatable viewpoints (Experiment 2). Furthermore, Experiment 2 revealed benefits associated with utilizing synthetic vision in both the PFD and ND representations and the value of combined ego- and exocentric presentations

Flight Test of a Head-Worn Display as an Equivalent-HUD for Terminal Operations

Research, development, test, and evaluation of flight deck interface technologies is being conducted by NASA to proactively identify, develop, and mature tools, methods, and technologies for improving overall aircraft safety of new and legacy vehicles operating in the Next Generation Air Transportation System (NextGen). Under NASA's Aviation Safety Program, one specific area of research is the use of small Head-Worn Displays (HWDs) as a potential equivalent display to a Head-up Display (HUD). Title 14 of the US CFR 91.175 describes a possible operational credit which can be obtained with airplane equipage of a HUD or an "equivalent"' display combined with Enhanced Vision (EV). A successful HWD implementation may provide the same safety and operational benefits as current HUD-equipped aircraft but for significantly more aircraft in which HUD installation is neither practical nor possible. A flight test was conducted to evaluate if the HWD, coupled with a head-tracker, can provide an equivalent display to a HUD. Approach and taxi testing was performed on-board NASA's experimental King Air aircraft in various visual conditions. Preliminary quantitative results indicate the HWD tested provided equivalent HUD performance, however operational issues were uncovered. The HWD showed significant potential as all of the pilots liked the increased situation awareness attributable to the HWD's unique capability of unlimited field-of-regard

Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation

Attention-related human performance limiting states (AHPLS) can cause pilots to lose airplane state awareness (ASA), and their detection is important to improving commercial aviation safety. The Commercial Aviation Safety Team found that the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness, and that distraction of various forms was involved in all of them. Research on AHPLS, including 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 has been implemented to simultaneously measure their physiological markers during high fidelity flight simulation human subject studies. Pilot participants were asked to perform benchmark tasks and experimental flight scenarios designed to induce AHPLS. Pattern classification was employed to distinguish the AHPLS induced by the benchmark tasks. Unimodal classification using pre-processed electroencephalography (EEG) signals as input features to extreme gradient boosting, random forest and deep neural network multiclass classifiers was implemented. Multi-modal classification using galvanic skin response (GSR) in addition to the same EEG signals and using the same types of classifiers produced increased accuracy with respect to the unimodal case (90 percent vs. 86 percent), although only via the deep neural network classifier. These initial results are a first step toward 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

Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

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

NASA\u27s Human Contributions To Safety Data Testbed

NASA’s System-Wide Safety (SWS) Project conducted a high-fidelity flightsimulation study and curated a publicly available data and analysis coderepository, referred to as the Human Contributions to Safety (HC2S) Data Testbed.Publication of the HC2S Data Testbed data descriptor publication in the NatureScientific Data journal provides a detailed description of the research dataset aswell as methodological procedures, data management, and analysis approaches.The purpose of the HC2S Data Testbed is to enable empirical assessment ofresilient pilot behaviors and broaden the understanding of human contributions tosafety in commercial aviation. By framing safety as the capacity to succeed undervarying conditions, this work contributes directly to emerging concepts in Safety-IIand advances development of an In-time Aviation Safety Management Systems(IASMS)