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An Unobtrusive System to Measure, Assess, and Predict Cognitive Workload in Real-World EnvironmentsAcross many careers, individuals face alternating periods of high and low attention and cognitive workload, which can result in impaired cognitive functioning and can be detrimental to job performance. For example, some professions (e.g., fire fighters, emergency medical personnel, doctors and nurses working in an emergency room, pilots) require long periods of low workload (boredom), followed by sudden, high-tempo operations during which they may be required to respond to an emergency and perform at peak cognitive levels. Conversely, other professions (e.g., air traffic controllers, market investors in financial industries, analysts) require long periods of high workload and multitasking during which the addition of just one more task results in cognitive overload resulting in mistakes. An unobtrusive system to measure, assess, and predict cognitive workload could warn individuals, their teammates, or their supervisors when steps should be taken to augment cognitive readiness. In this talk I will describe an approach to this problem that we have found to be successful across work domains including: (1) a suite of unobtrusive, field-ready neurophysiological, physiological, and behavioral sensors that are chosen to best suit the target environment; (2) custom algorithms and statistical techniques to process and time-align raw data originating from the sensor suite; (3) probabilistic and statistical models designed to interpret the data into the human state of interest (e.g., cognitive workload, attention, fatigue); (4) and machine-learning techniques to predict upcoming performance based on the current pattern of events, and (5) display of each piece of information depending on the needs of the target user who may or may not want to drill down into the functioning of the system to determine how conclusions about human state and performance are determined. I will then focus in on our experimental results from our custom functional near-infrared spectroscopy sensor, designed to operate in real-world environments to be worn comfortably (e.g., positioned into a baseball cap or a surgeons cap) to measure changes in brain blood oxygenation without adding burden to the individual being assessed.
Document ID
20170009837
Acquisition Source
Ames Research Center
Document Type
Presentation
Authors
Bracken, Bethany K.
(Charles River Analytics, Inc. Cambridge, MA, United States)
Palmon, Noa
(Charles River Analytics, Inc. Cambridge, MA, United States)
Elkin-Frankston, Seth
(Charles River Analytics, Inc. Cambridge, MA, United States)
Irvin, Scott
(Charles River Analytics, Inc. Cambridge, MA, United States)
Jenkins, Michael
(Charles River Analytics, Inc. Cambridge, MA, United States)
Farry, Mike
(Charles River Analytics, Inc. Cambridge, MA, United States)
Date Acquired
October 11, 2017
Publication Date
February 23, 2017
Subject Category
Statistics And Probability
Computer Programming And Software
Behavioral Sciences
Report/Patent Number
ARC-E-DAA-TN39136
Meeting Information
Meeting: International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) 2017
Location: Porto
Country: Portugal
Start Date: February 21, 2017
End Date: February 23, 2017
Sponsors: Institute for Systems and Technologies of Information (INSTICC)
Funding Number(s)
CONTRACT_GRANT: NNX16CJ08C
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
workload collection
unobtrusive measure
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