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 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.

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 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.

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.

Exploring The Effect of Visual Information Degradation on Human Perception and Performance In A Human-Telerobot System

2020 ◽  
Vol 64 (1) ◽  
pp. 1302-1307
Author(s):  
Richard Stone ◽  
Minglu Wang ◽  
Thomas Schnieders ◽  
Esraa Abdelall
Keyword(s):  
Visual Information ◽  
Human Perception ◽  
Low Level ◽  
Hit Rate ◽  
Word Level ◽  
Level Of Automation ◽  
Significant Difference ◽  
Human Operators ◽  
And Performance ◽  
High Level

Human-robotic interaction system are increasingly becoming integrated into industrial, commercial and emergency service agencies. It is critical that human operators understand and trust automation when these systems support and even make important decisions. The following study focused on human-in-loop telerobotic system performing a reconnaissance operation. Twenty-four subjects were divided into groups based on level of automation (Low-Level Automation (LLA), and High-Level Automation (HLA)). Results indicated a significant difference between low and high word level of control in hit rate when permanent error occurred. In the LLA group, the type of error had a significant effect on the hit rate. In general, the high level of automation was better than the low level of automation, especially if it was more reliable, suggesting that subjects in the HLA group could rely on the automatic implementation to perform the task more effectively and more accurately.

Understanding aviation operators’ variability in advanced systems

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maria Papanikou ◽  
Utku Kale ◽  
András Nagy ◽  
Konstantinos Stamoulis
Keyword(s):  
Human Performance ◽  
Situational Awareness ◽  
Air Traffic ◽  
Content Type ◽  
Homogeneous Groups ◽  
Professional Groups ◽  
Performance Shaping Factors ◽  
Generational Changes ◽  
And Performance ◽  
Practical Implications

Purpose This study aims to identify variability in aviation operators in order to gain greater understanding of the changes in aviation professional groups. Research has commonly addressed human factors and automation in broad categories according to a group’s function (e.g., pilots, air traffic controllers [ATCOs], engineers). Accordingly, pilots and Air Traffic Controls (ATCOs) have been treated as homogeneous groups with a set of characteristics. Currently, critical themes of human performance in light of systems’ developments place the emphasis on quality training for improved situational awareness (SA), decision-making and cognitive load. Design/methodology/approach As key solutions centre on the increased understanding and preparedness of operators through quality training, the authors deploy an iterative mixed methodology to reveal generational changes of pilots and ATCOs. In total, 46 participants were included in the qualitative instrument and 70 in the quantitative one. Preceding their triangulation, the qualitative data were analysed using NVivo and the quantitative analysis was aided through descriptive statistics. Findings The results show that there is a generational gap between old and new generations of operators. Although positive views on advanced systems are being expressed, concerns about cognitive capabilities in the new systems, training and skills gaps, workload and role implications are presented. Practical implications The practical implications of this study extend to different profiles of operators that collaborate either directly or indirectly and that are critical to aviation safety. Specific implications are targeted on automation complacency, bias and managing information load, and training aspects where quality training can be aided by better understanding the occupational transitions under advanced systems. Originality/value In this paper, the authors aimed to understand the changing nature of the operators’ profession within the advanced technological context, and the perceptions and performance-shaping factors of pilots and ATCOs to define the generational changes.

scholarly journals Adventures in Music Translation: Italian Songs and Arias (aka “The Yellow Book”) and Me

2017 ◽  
Vol 9 (1) ◽  
pp. 63
Author(s):  
Lynn Penrod
Keyword(s):  
Art Song ◽  
Translation Studies ◽  
Collaborative Approach ◽  
The Future ◽  
Working Together ◽  
Italian Songs ◽  
And Performance ◽  
Critical Interest

This article is a general exploration of translation issues involved in the translation and performance of the art song, arguing that although critical interest in recent years has been growing, the problems involved in these hybrid translation projects involving both text and music present a number of conundrums: primacy of text or music, focus on performability, and age-old arguments about fidelity and/or foreignization vs domestication. Using information from theatre translation and input from singers themselves, the author argues that this particular area of translation studies will work best in the future with a collaborative approach that includes translators, musicologists, and performers working together in order to produce the most “singable” text as possible for the art song in performance.

scholarly journals TESSA: design and implementation of a platform for situational sea awareness

2017 ◽  
Vol 17 (2) ◽  
pp. 185-196
Author(s):  
Mario Scalas ◽  
Palmalisa Marra ◽  
Luca Tedesco ◽  
Raffaele Quarta ◽  
Emanuele Cantoro ◽  
...  
Keyword(s):  
Situational Awareness ◽  
Building Blocks ◽  
Ministry Of Education ◽  
End User ◽  
Performance Requirements ◽  
Platform Architecture ◽  
Front End ◽  
Critical Issues ◽  
And Performance ◽  
Key Aspects

Abstract. This article describes the architecture of sea situational awareness (SSA) platform, a major asset within TESSA, an industrial research project funded by the Italian Ministry of Education and Research. The main aim of the platform is to collect, transform and provide forecast and observational data as information suitable for delivery across a variety of channels, like web and mobile; specifically, the ability to produce and provide forecast information suitable for creating SSA-enabled applications has been a critical driving factor when designing and evolving the whole architecture. Thus, starting from functional and performance requirements, the platform architecture is described in terms of its main building blocks and flows among them: front-end components that support end-user applications and map and data analysis components that allow for serving maps and querying data. Focus is directed to key aspects and decisions about the main issues faced, like interoperability, scalability, efficiency and adaptability, but it also considers insights about future works in this and similarly related subjects. Some analysis results are also provided in order to better characterize critical issues and related solutions.

A Human Arm’s Mechanical Impedance Tuning Method for Improving the Stability of Haptic Rendering

Robotica ◽  
2020 ◽  
pp. 1-13
Author(s):  
Xiong Lu ◽  
Beibei Qi ◽  
Hao Zhao ◽  
Junbin Sun
Keyword(s):  
Degrees Of Freedom ◽  
External Disturbance ◽  
Mechanical Impedance ◽  
System Stability ◽  
Tuning Method ◽  
Rigid Objects ◽  
Human Arm ◽  
Human Operators ◽  
And Performance ◽  
The Stability

SUMMARY Rendering of rigid objects with high stiffness while guaranteeing system stability remains a major and challenging issue in haptics. Being a part of the haptic system, the behavior of human operators, represented as the mechanical impedance of arm, has an inevitable influence on system performance. This paper first verified that the human arm impedance can unconsciously be modified through imposing background forces and resist unstable motions arising from external disturbance forces. Then, a reliable impedance tuning (IT) method for improving the stability and performance of haptic systems is proposed, which tunes human arm impedance by superimposing a position-based background force over the traditional haptic workspace. Moreover, an adaptive IT algorithm, adjusting the maximum background force based on the velocity of the human arm, is proposed to achieve a reasonable trade-off between system stability and transparency. Based on a three-degrees-of-freedom haptic device, maximum achievable stiffness and transparency grading experiments are carried out with 12 subjects, which verify the efficacy and advantage of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document