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Optimizing Human-Automation Team Workload through a Non-Invasive Detection System

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: D17PC00119
Agency Tracking Number: D16C-003-0019
Amount: $149,598.40
Phase: Phase I
Program: STTR
Solicitation Topic Code: ST16C-003
Solicitation Number: 2016.0
Timeline
Solicitation Year: 2016
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-03-15
Award End Date (Contract End Date): 2018-04-07
Small Business Information
1650 South Amphlett Blvd.
San Mateo, CA 94402
United States
DUNS: 608176715
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Terrance (T.J.) Goan
 Principal Investigator
 (206) 430-7733
 goan@stottlerhenke.com
Business Contact
 Carolyn Maxwell
Phone: (650) 931-2700
Email: carolyn@stottlerhenke.com
Research Institution
 Massachusetts General Hospital
 Susan Buchan
 
399 Revolution Drive
Somerville, MA 02145
United States

 (857) 282-1718
 Domestic nonprofit research organization
Abstract

We propose to investigate, in collaboration with the Massachusetts General Hospital Voice Center and Altec, Inc., the application of surface electromyography (sEMG) to assessing cognitive workload, strain, and overload. Specifically, sEMG sensors placed on the face and neck will detect emotional/motor responses to workload strain. The proposed effort will build on the substantial sEMG experience of our partners (including their research on vocal/subvocal speech recognition) as well as the technical foundation of Altecs unobtrusive, wireless sEMG sensing and signal processing equipment and our state-of-the-art technology for real-time human state assessment. Ultimately, this effort will result in a robust, reliable, and highly sensitive model for estimating workload strain and detecting and predicting cognitive overload in real time. The system will employ face/neck sEMG along with a complement of cost-effective sensors that can be unobtrusively integrated into the operators environment while minimizing artificial constraints on operator behavior. It will also utilize additional sources of evidence that reflect an operators internal state (e.g., attention dynamics, physical performance, and status of the automation involved). Phase I will demonstrate the utility of sEMG for this application and pave the way for Phase II prototype development and evaluation within an operationally relevant tasking environment.

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

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