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Methods for Actionable Measures of Absolute Cognitive Workload

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-16-C-0217
Agency Tracking Number: N16A-002-0127
Amount: $79,977.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N16A-T002
Solicitation Number: 2016.0
Timeline
Solicitation Year: 2016
Award Year: 2016
Award Start Date (Proposal Award Date): 2016-05-11
Award End Date (Contract End Date): 2016-12-19
Small Business Information
13900 County Road 455
Clermont, FL 34711
United States
DUNS: 034547503
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Dr. Peter Crane
 (602) 312-6001
 petercrane.az@gmail.com
Business Contact
 Liz Alessi
Phone: (407) 310-3440
Email: lizaSBIR@virtualrealityrehab.com
Research Institution
 Emry Riddle Aerinatical
 Dr. Jason Kring
 
600 South Clyde Morris Blvd \N
Daytona Beach, FL 32114
United States

 (386) 323-8045
 Nonprofit College or University
Abstract

Cognitive workload is the amount of mental effort an individual must exert to perform a required set of tasks. The proposed methods for actionable measures of absolute cognitive workload will integrate subjective workload ratings together with real-time physiological measures and assessments of mission effectiveness based on carefully designed scenarios. These data will be used as input parameters in mathematical models of workload that can then be used to predict workload in development of future systems. This approach is based on both established science and demonstrated technology and will provide the Navy and commercial users with a toolset that will help to enhance mission effectiveness and reduce life-cycle costs. The primary benefit of this technology is reduced cost and risk in developing future mission systems and operator interfaces. Engineers and designers can model the effects of different designs on operator workload long before prototyping when design changes are simple and inexpensive. Workload modeling and prediction will have great commercial potential particularly for emerging technologies where determinants of workload demands are not well understood such as development of user interfaces and operating procedures for commercial Remotely Piloted Aircraft. Using workload modeling and prediction will reduce development cost and enhance system safety.

* Information listed above is at the time of submission. *

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