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Multimodal physiological monitoring during virtual reality piloting tasks

August 25, 2022
  |
Journal Article
Author:
Hrishikesh Mohan Rao
…
Published in:
Multimodal Physiological Monitoring During Virtual Reality Piloting Tasks (version 1.0.0). PhysioNet. https://doi-org.ezproxy.canberra.edu.au/10.13026/azwa-ge48.
Topic:
health
R&D area:
R&D group:

Summary

This dataset includes multimodal physiologic, flight performance, and user interaction data streams, collected as participants performed virtual flight tasks of varying difficulty. In virtual reality, individuals flew an "Instrument Landing System" (ILS) protocol, in which they had to land an aircraft mostly relying on the cockpit instrument readings. Participants were presented with four levels of difficulty, which were generated by varying wind speed, turbulence, and visibility. Each of the participants performed 12 runs, split into 3 blocks of four consecutive runs, one run at each difficulty, in a single experimental session. The sequence of difficulty levels was presented in a counterbalanced manner across blocks. Flight performance was quantified as a function of horizontal and vertical deviation from an ideal path towards the runway as well as deviation from the prescribed ideal speed of 115 knots. Multimodal physiological signals were aggregated and synchronized using Lab Streaming Layer. Descriptions of data quality are provided to assess each data stream. The starter code provides examples of loading and plotting the time synchronized data streams, extracting sample features from the eye tracking data, and building models to predict pilot performance from the physiology data streams.
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Summary

This dataset includes multimodal physiologic, flight performance, and user interaction data streams, collected as participants performed virtual flight tasks of varying difficulty. In virtual reality, individuals flew an "Instrument Landing System" (ILS) protocol, in which they had to land an aircraft mostly relying on the cockpit instrument readings. Participants were...

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Multimodal physiological monitoring during virtual reality piloting tasks

The Human Trafficking Technology Roadmap: a targeted development strategy for the Department of Homeland Security

July 31, 2019
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Technical Report
Author:
Matthew P. Daggett
…
Published in:
MIT Lincoln Laboratory Report
Topic:
human trafficking
R&D area:
R&D group:

Summary

Human trafficking is a form of modern-day slavery that involves the use of force, fraud, or coercion for the purposes of involuntary labor and sexual exploitation. It affects tens of million of victims worldwide and generates tens of billions of dollars in illicit profits annually. While agencies across the U.S. Government employ a diverse range of resources to combat human trafficking in the U.S. and abroad, trafficking operations remain challenging to measure, investigate, and interdict. Within the Department of Homeland Security, the Science and Technology Directorate is addressing these challenges by incorporating computational social science research into their counter-human trafficking approach. As part of this approach, the Directorate tasked an interdisciplinary team of national security researchers at the Massachusetts Institute of Technology's Lincoln Laboratory, a federally funded research and development center, to undertake a detailed examination of the human trafficking response across the Homeland Security Enterprise. The first phase of this effort was a government-wide systems analysis of major counter-trafficking thrust areas, including law enforcement and prosecution; public health and emergency medicine; victim services; and policy and legislation. The second phase built on this systems analysis to develop a human trafficking technology roadmap and implementation strategy for the Science and Technology Directorate, which is presented in this document.
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Summary

Human trafficking is a form of modern-day slavery that involves the use of force, fraud, or coercion for the purposes of involuntary labor and sexual exploitation. It affects tens of million of victims worldwide and generates tens of billions of dollars in illicit profits annually. While agencies across the U.S...

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The Human Trafficking Technology Roadmap: a targeted development strategy for the Department of Homeland Security

AI enabling technologies: a survey

May 8, 2019
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Journal Article
Author:
Vijay N. Gadepally
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Published in:
https://arxiv.org/abs/1905.03592
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

Artificial Intelligence (AI) has the opportunity to revolutionize the way the United States Department of Defense (DoD) and Intelligence Community (IC) address the challenges of evolving threats, data deluge, and rapid courses of action. Developing an end-to-end artificial intelligence system involves parallel development of different pieces that must work together in order to provide capabilities that can be used by decision makers, warfighters and analysts. These pieces include data collection, data conditioning, algorithms, computing, robust artificial intelligence, and human-machine teaming. While much of the popular press today surrounds advances in algorithms and computing, most modern AI systems leverage advances across numerous different fields. Further, while certain components may not be as visible to end-users as others, our experience has shown that each of these interrelated components play a major role in the success or failure of an AI system. This article is meant to highlight many of these technologies that are involved in an end-to-end AI system. The goal of this article is to provide readers with an overview of terminology, technical details and recent highlights from academia, industry and government. Where possible, we indicate relevant resources that can be used for further reading and understanding.
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Summary

Artificial Intelligence (AI) has the opportunity to revolutionize the way the United States Department of Defense (DoD) and Intelligence Community (IC) address the challenges of evolving threats, data deluge, and rapid courses of action. Developing an end-to-end artificial intelligence system involves parallel development of different pieces that must work together...

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AI enabling technologies: a survey

Cognitive workload and visual attention analyses of the air traffic control Tower Flight Data Manager (TFDM) prototype demonstration

October 22, 2012
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Conference Paper
Author:
Kiran J. Lokhande
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Published in:
HFES 2012, Human Factors and Ergonomics Society 56th Annual Mtg., 22-26 October 2012.
Topic:
air traffic control
R&D area:
R&D group:

Summary

This paper presents two methods of analyzing air traffic controller activity: cognitive workload measurement through the novel comparison of controller-pilot verbal communications, and visual attention quantification through manual eye gaze analysis. These analyses were performed as part of an evaluation of the Tower Flight Data Manager (TFDM) prototype system. Cognitive workload analyses revealed that, when comparing participant controllers utilizing TFDM to a control group utilizing existing air traffic control (ATC) equipment, participants issued commands sooner than the control, and thus were perceived to have a lower workload. While visual attention data were not available for the control group, analyses of participant gaze data revealed 81.9% of time was spent in a head-down position, and 17.2% of time was spent head-up. Results are related back to system inefficiencies to find potential areas of improvement in design.
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Summary

This paper presents two methods of analyzing air traffic controller activity: cognitive workload measurement through the novel comparison of controller-pilot verbal communications, and visual attention quantification through manual eye gaze analysis. These analyses were performed as part of an evaluation of the Tower Flight Data Manager (TFDM) prototype system. Cognitive...

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Cognitive workload and visual attention analyses of the air traffic control Tower Flight Data Manager (TFDM) prototype demonstration

Dallas/Fort Worth field demonstration #2 (DFW-2) final report for Tower Flight Data Manager (TFDM)

May 16, 2012
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Project Report
Author:
Hayley J. Reynolds
…
Published in:
MIT Lincoln Laboratory Report ATC-393
Topic:
air traffic control
R&D area:

Summary

The Tower Flight Data Manager (TFDM) is the next generation air traffic control tower (ATCT) information system that integrates surveillance, flight data, and other sources, which enables advanced decision support tools (DSTs) to improve departure and arrival efficiency and reduce fuel burn at the airport. TFDM was exercised as a prototype installed at the Dallas / Fort Worth International Airport (DFW) during a two-week demonstration in the spring of 2011 termed DFW-2. MIT Lincoln Laboratory conducted this demonstration for the FAA in coordination with DFW air traffic control (ATC) and the DFW airport authority. The objective of this TFDM field demonstration was to validate the operational suitability and refine production system requirements of the Tower Information Display System (TIDS) surface surveillance display and Flight Data Manager (FDM) electronic flight data display and to evaluate the first iteration of the Supervisor Display and DSTs. These objectives were met during the two-week field demonstration. Results indicated that the TIDS and FDM exhibited capabilities considered operationally suitable for the tower as an advisory system and as a primary means for control given surface surveillance that is approved for operational use. Human factors data indicated that TIDS and FDM could be beneficial. The prototype Supervisor Display and DSTs met a majority of the technical performance criteria, but fewer than half of the human factors success criteria were met. As this was the first iteration of the Supervisor Display and DST capabilities, subsequent prototype iterations to achieve the target concept of operations, functionality and information presentation with accompanying field demonstrations to evaluate these honed capabilities were recommended and expected. FLM/TMC feedback will help refine subsequent system design.
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Summary

The Tower Flight Data Manager (TFDM) is the next generation air traffic control tower (ATCT) information system that integrates surveillance, flight data, and other sources, which enables advanced decision support tools (DSTs) to improve departure and arrival efficiency and reduce fuel burn at the airport. TFDM was exercised as a...

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Dallas/Fort Worth field demonstration #2 (DFW-2) final report for Tower Flight Data Manager (TFDM)

The Tower Flight Data Manager prototype system

October 16, 2011
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Conference Paper
Author:
Vineet Mehta
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Published in:
DASC 2011, 30th IEEE/AIAA Digital Avionics Systems Conference, 16-20 October 2011, pp. 2C5.
Topic:
air traffic control
R&D area:
R&D group:

Summary

The Tower Flight Data Manager (TFDM) will serve as the next generation air traffic control tower automation platform for surface and local airspace operations. TFDM provides three primary enhancements over current systems: consolidation of diverse data and information sources into a single platform; electronic data exchange, including flight data entries, within and outside the tower cab; and a suite of decision support capabilities leveraging TFDM's access to external data sources and systems. This paper describes a TFDM prototype system that includes integrated surveillance, flight data, and decision support display components. Enhancements in airport configuration management, runway assignment, taxi routing, sequencing and scheduling, and departure route assurance are expected to yield significant benefits in delay reduction, fuel savings, additional capacity, improved access, enhanced safety, and reduced environmental impact. Data are provided on system performance and air traffic controller acceptance from simulation studies and a preliminary field demonstration at Dallas / Ft. Worth International Airport.
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Summary

The Tower Flight Data Manager (TFDM) will serve as the next generation air traffic control tower automation platform for surface and local airspace operations. TFDM provides three primary enhancements over current systems: consolidation of diverse data and information sources into a single platform; electronic data exchange, including flight data entries...

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The Tower Flight Data Manager prototype system

A field demonstration of the air traffic control Tower Flight Data Manager prototype

September 19, 2011
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Conference Paper
Author:
Hayley J. Reynolds
…
Published in:
HFES 2011, Human Factors and Ergonomics Society 55th Annual Mtg., 19-23 September 2011, p. 61-65.
Topic:
air traffic control
R&D area:
R&D group:

Summary

The development and evaluation process of the Tower Flight Data Manager prototype at Dallas Ft. Worth airport is described. Key results from the first field evaluation are presented, including lessons learned about making electronic flight information acceptable to controllers. Iteration of the field evaluation methods are discussed for practitioner benefit.
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Summary

The development and evaluation process of the Tower Flight Data Manager prototype at Dallas Ft. Worth airport is described. Key results from the first field evaluation are presented, including lessons learned about making electronic flight information acceptable to controllers. Iteration of the field evaluation methods are discussed for practitioner benefit.

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A field demonstration of the air traffic control Tower Flight Data Manager prototype

Concept of operations for the Integrated Departure Route Planning (IDRP) tool

September 12, 2011
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Project Report
Author:
Hayley J. Reynolds
…
Published in:
MIT Lincoln Laboratory Report ATC-379
Topic:
decision support
R&D area:
R&D group:

Summary

A concept of operations for the Integrated Departure Route Planner (IDRP) tool is proposed to address issues in the area of departure route management. By combining information about weather and departure demand, IDRP can both identify potential demand/capacity imbalances and recommend a rerouting option, if appropriate. To effectively implement IDRP into the operational environment, a twophase approach is suggested. The first phase appends IDRP functionality onto the CIWS/RAPT platform, combining departure demand information with the convective weather information, creating a live prototype. This initial phase allows a gradual introduction of functionality into an existing display and enables the gathering of operational data to appropriately evolve IDRP to phase 2. The second phase involves introducing airline route preferences, along with any operational improvements discovered during the initial phase.
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Summary

A concept of operations for the Integrated Departure Route Planner (IDRP) tool is proposed to address issues in the area of departure route management. By combining information about weather and departure demand, IDRP can both identify potential demand/capacity imbalances and recommend a rerouting option, if appropriate. To effectively implement IDRP...

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Concept of operations for the Integrated Departure Route Planning (IDRP) tool

Uses for field communication data in designing air traffic management decision support

May 31, 2011
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Conference Paper
Author:
Hayley J. Reynolds
…
Published in:
10th Conf. on Naturalistic Decision Making, 31 May 2011.
Topic:
air traffic management
R&D area:
R&D group:

Summary

In this paper, example uses of field communication data are provided and how these data impact the evolution of the Route Availability Planning Tool (RAPT) for air traffic management is introduced. Simple communications analyses are provided that illustrate how communications can be used to improve what decision support is provided, who it is provided to, and in what context to present the support. Communications data is also shown to aid in contextualizing the decision support to better fit within the decision support framework in existence, which is critical to the success of situation awareness systems.
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Summary

In this paper, example uses of field communication data are provided and how these data impact the evolution of the Route Availability Planning Tool (RAPT) for air traffic management is introduced. Simple communications analyses are provided that illustrate how communications can be used to improve what decision support is provided...

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Uses for field communication data in designing air traffic management decision support

Field & (data) stream: a method for functional evolution of the Air Traffic Management Route Availability Planning Tool (RAPT)

September 27, 2010
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Conference Paper
Author:
Hayley J. Reynolds
…
Published in:
HFES 2010, Proc. of the 54th Human Factors and Ergonomics Society Annual Mtg., 27 September 2010, pp. 104-108.
Topic:
air traffic control
R&D area:
R&D group:

Summary

A method coupling field evaluation with operations data analysis is presented as an effective means to functionally evolve a decision support system. The case study used to illustrate this method is the evaluation of the Route Availability Planning Tool (RAPT), a decision support tool to improve departure efficiency in convective weather in New York air traffic facilities. It was only through a combination of quantitative performance data analysis and field observations to identify key elements of the decision making process that the designers were able to determine the most critical departure management decision requiring support, leading to significant improvements in departure efficiency.
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Summary

A method coupling field evaluation with operations data analysis is presented as an effective means to functionally evolve a decision support system. The case study used to illustrate this method is the evaluation of the Route Availability Planning Tool (RAPT), a decision support tool to improve departure efficiency in convective...

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Field & (data) stream: a method for functional evolution of the Air Traffic Management Route Availability Planning Tool (RAPT)
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