scholarly journals Real-Time Prediction of Fluctuations in Cognitive Workload

2019 ◽  
Author(s):  
Udo Boehm ◽  
Dora Matzke ◽  
Matthew Benjamin Gretton ◽  
Spencer Castro ◽  
Joel Cooper ◽  
...  
Keyword(s):  
Real Time ◽  
Situational Awareness ◽  
Predictive Ability ◽  
Cognitive Workload ◽  
Time Prediction ◽  
Large Fluctuations ◽  
Human Operators ◽  
Time Changes ◽  
Automated Support ◽  
Response Task

Human operators often experience large fluctuations in cognitive workload that can lead to sub-optimal performance, ranging from overload to neglect. Help from automated support systems could potentially address this issue, but to do so the system would ideally need to be aware of real-time changes in operators’ cognitive workload, so it can provide help in times of peak demand and take advantage of troughs to elicit operator engagement. We used the ISO standard Detection Response Task (DRT) to measure cognitive workload approximately every 4 seconds in a demanding task requiring monitoring and refuelling of a fleet of unmanned aerial vehicles (UAVs). We showed that the DRT provided a valid measure that can detect changes in workload due to changes in the number of UAVs. We used a cross-validation analysis to assess whether measures related to task performance immediately preceding the DRT could be used to predict detection performance as a proxy for cognitive workload. Although the simple occurrence of task events had weak predictive ability, composite measures that tapped operators’ situational awareness with respect to fuel levels were much more effective. We conclude that real-time prediction of operators’ cognitive workload shows promise as an avenue for improved human-automation teaming.

scholarly journals Real-time prediction of short-timescale fluctuations in cognitive workload

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Udo Boehm ◽  
Dora Matzke ◽  
Matthew Gretton ◽  
Spencer Castro ◽  
Joel Cooper ◽  
...  
Keyword(s):  
Real Time ◽  
Situational Awareness ◽  
Predictive Ability ◽  
Task Demands ◽  
Cognitive Capacity ◽  
Cognitive Workload ◽  
Spare Capacity ◽  
Human Operators ◽  
Time Changes ◽  
Response Task

AbstractHuman operators often experience large fluctuations in cognitive workload over seconds timescales that can lead to sub-optimal performance, ranging from overload to neglect. Adaptive automation could potentially address this issue, but to do so it needs to be aware of real-time changes in operators’ spare cognitive capacity, so it can provide help in times of peak demand and take advantage of troughs to elicit operator engagement. However, it is unclear whether rapid changes in task demands are reflected in similarly rapid fluctuations in spare capacity, and if so what aspects of responses to those demands are predictive of the current level of spare capacity. We used the ISO standard detection response task (DRT) to measure cognitive workload approximately every 4 s in a demanding task requiring monitoring and refueling of a fleet of simulated unmanned aerial vehicles (UAVs). We showed that the DRT provided a valid measure that can detect differences in workload due to changes in the number of UAVs. We used cross-validation to assess whether measures related to task performance immediately preceding the DRT could predict detection performance as a proxy for cognitive workload. Although the simple occurrence of task events had weak predictive ability, composite measures that tapped operators’ situational awareness with respect to fuel levels were much more effective. We conclude that cognitive workload does vary rapidly as a function of recent task events, and that real-time predictive models of operators’ cognitive workload provide a potential avenue for automation to adapt without an ongoing need for intrusive workload measurements.

scholarly journals Real-Time Prediction of Short Time Scale Fluctuations in Cognitive Workload

2021 ◽  
Author(s):  
Udo Boehm ◽  
Dora Matzke ◽  
Matthew Benjamin Gretton ◽  
Spencer C. Castro ◽  
Joel Cooper ◽  
...  
Keyword(s):  
Real Time ◽  
Situational Awareness ◽  
Predictive Ability ◽  
Task Demands ◽  
Cognitive Capacity ◽  
Short Time Scale ◽  
Cognitive Workload ◽  
Spare Capacity ◽  
Human Operators ◽  
Response Task

Human operators often experience large fluctuations in cognitive workload over seconds time scales that can lead to sub-optimal performance, ranging from overload to neglect. Adaptive automation could potentially address this issue, but to do so it needs to be aware of real-time changes in operators’ spare cognitive capacity, so it can provide help in times of peak demand and take advantage of troughs to elicit operator engagement. However, it is unclear whether rapid changes in task demands are reflected in similarly rapid fluctuations in spare capacity, and if so what aspects of responses to those demands are predictive the current level of spare capacity. We used the ISO standard Detection Response Task (DRT) to measure cognitive workload approximately every 4 seconds in a demanding task requiring monitoring and refuelling of a fleet of simulated unmanned aerial vehicles (UAVs). We showed that the DRT provided a valid measure that can detect differences in workload due to changes in the number of UAVs. We used cross-validation to assess whether measures related to task performance immediately preceding the DRT could predict detection performance as a proxy for cognitive workload. Although the simple occurrence of task events had weak predictive ability, composite measures that tapped operators’ situational awareness with respect to fuel levels were much more effective. We conclude that cognitive workload does vary rapidly as a function of recent task events, and that real-time predictive models of operators’ cognitive workload provide a potential avenue for automation to adapt without an ongoing need for intrusive workload measurements.

A Cross-Domain Approach to Designing an Unobtrusive System to Assess Human State and Predict Upcoming Performance Deficits

2016 ◽  
Vol 60 (1) ◽  
pp. 707-711 ◽  
Author(s):  
Bethany Bracken ◽  
Noa Palmon ◽  
Lee Kellogg ◽  
Seth Elkin-Frankston ◽  
Michael Farry
Keyword(s):  
Situational Awareness ◽  
Work Environments ◽  
Cognitive Workload ◽  
Performance Deficits ◽  
Cognitive Readiness ◽  
Cross Domain ◽  
Mission Success ◽  
Working Together ◽  
Human Operators ◽  
And Performance

Many work environments are fraught with highly variable demands on cognitive workload, fluctuating between periods of high operational demand to the point of cognitive overload, to long periods of low workload bordering on boredom. When cognitive workload is not in an optimal range at either end of the spectrum, it can be detrimental to situational awareness and operational readiness, resulting in impaired cognitive functioning (Yerkes and Dodson, 1908). An unobtrusive system to assess the state of the human operator (e.g., stress, cognitive workload) and predict upcoming performance deficits could warn operators when steps should be taken to augment cognitive readiness. This system would also be useful during testing and evaluation (T&E) when new tools and systems are being evaluated for operational use. T&E researchers could accurately evaluate the cognitive and physical demands of these new tools and systems, and the effects they will have on task performance and accuracy. In this paper, we describe an approach to designing such a system that is applicable across environments. First, a suite of sensors is used to perform real-time synchronous data collection in a robust and unobtrusive fashion, and provide a holistic assessment of operators. Second, the best combination of indicators of operator state is extracted, fused, and interpreted. Third, performance deficits are comprehensively predicted, optimizing the likelihood of mission success. Finally, the data are displayed in such a way that supports the information requirements of any user. The approach described here is one we have successfully used in several projects, including modeling cognitive workload in the context of high-tempo, physically demanding environments, and modeling individual and team workload, stress, engagement, and performance while working together on a computerized task. We believe this approach is widely applicable and useful across domains to dramatically improve the mission readiness of human operators, and will improve the design and development of tools available to assist the operator in carrying out mission objectives. A system designed using this approach could enable crew to be aware of impending deficits to aid in augmenting mission performance, and will enable more effective T&E by measuring workload in response to new tools and systems while they are being designed and developed, rather than once they are deployed.

scholarly journals Correction to: Real-time prediction of short-timescale fluctuations in cognitive workload

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Udo Boehm ◽  
Dora Matzke ◽  
Matthew Gretton ◽  
Spencer Castro ◽  
Joel Cooper ◽  
...  
Keyword(s):  
Real Time ◽  
Cognitive Workload ◽  
Time Prediction ◽  
Short Timescale

Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 41-57
Author(s):  
Manisha Mishra ◽  
Pujitha Mannaru ◽  
David Sidoti ◽  
Adam Bienkowski ◽  
Lingyi Zhang ◽  
...  
Keyword(s):  
Decision Support ◽  
Situational Awareness ◽  
Feature Space ◽  
Human Interaction ◽  
Superior Performance ◽  
Uncertain Environments ◽  
Cognitive States ◽  
Contextual Elements ◽  
Human Operators ◽  
Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances auto­nomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

Real-Time Prediction of Tropical Cyclone Intensity Using COAMPS-TC

2012 ◽  
Author(s):  
J. D. Doyle ◽  
R. M. Hodur ◽  
S. Chen ◽  
H. Jin ◽  
Y. Jin ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Real Time ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity ◽  
Time Prediction

scholarly journals Communication infrastructure for situational awareness enhancement in WAMS with optimal PMU placement

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Bhargav Appasani ◽  
Amitkumar Vidyakant Jha ◽  
Sunil Kumar Mishra ◽  
Abu Nasar Ghazali
Keyword(s):  
Real Time ◽  
Situational Awareness ◽  
Area Measurement ◽  
Measurement Unit ◽  
Monitoring And Control ◽  
Communication Infrastructure ◽  
Wide Area Measurement System ◽  
Digital Elevation ◽  
Elevation Model ◽  
And Control

AbstractReal time monitoring and control of a modern power system has achieved significant development since the incorporation of the phasor measurement unit (PMU). Due to the time-synchronized capabilities, PMU has increased the situational awareness (SA) in a wide area measurement system (WAMS). Operator SA depends on the data pertaining to the real-time health of the grid. This is measured by PMUs and is accessible for data analytics at the data monitoring station referred to as the phasor data concentrator (PDC). Availability of the communication system and communication delay are two of the decisive factors governing the operator SA. This paper presents a pragmatic metric to assess the operator SA and ensure optimal locations for the placement of PMUs, PDC, and the underlying communication infrastructure to increase the efficacy of operator SA. The uses of digital elevation model (DEM) data of the surface topography to determine the optimal locations for the placement of the PMU, and the microwave technology for communicating synchrophasor data is another important contribution carried out in this paper. The practical power grid system of Bihar in India is considered as a case study, and extensive simulation results and analysis are presented for validating the proposed methodology.

scholarly journals Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network

2021 ◽  
Author(s):  
Yanfei Guan ◽  
S. V. Shree Sowndarya ◽  
Liliana C. Gallegos ◽  
Peter C. St. John ◽  
Robert S. Paton
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Quantum Chemical ◽  
Organic Molecules ◽  
Chemical Shifts ◽  
13C Chemical Shifts ◽  
Time Prediction ◽  
Nmr Data ◽  
Proton Chemical Shifts

From quantum chemical and experimental NMR data, a 3D graph neural network, CASCADE, has been developed to predict carbon and proton chemical shifts. Stereoisomers and conformers of organic molecules can be correctly distinguished.

Sign in / Sign up

Export Citation Format

Share Document