SMOLT: Sensitive Mental Workload Assessment Enhanced with Multi-Task Learning

Award Information
Agency: Department of Defense
Branch: Army
Contract: W91CRB-13-C-0005
Agency Tracking Number: A122-087-0434
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2013
Solicitation Year: 2012
Solicitation Topic Code: A12-087
Solicitation Number: 2012.2
Small Business Information
15400 Calhoun Drive, Suite 400, Rockville, MD, -
DUNS: 161911532
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Guangfan Zhang
 Program Manager
 (301) 294-5244
Business Contact
 Mark James
Title: Director, Contracts and Proposals
Phone: (301) 294-5221
Research Institution
Mental workload is considered one of the most important contributors to human performance. In previous decades, considerable research has been conducted on workload assessment using different methods, such as subjective measurement and performance measurement. Recently, there is a trend to utilize physiological parameters, such as Electroencephalography (EEG) and Electrocardiography (ECG), for automatic objective workload assessment. Although significant progress has been made on physiological parameter based workload assessment, there are still a number of challenges to be addressed for automated workload assessment, two of which are: workload assessment in multiple dimensions (such as visual and cognitive) and high performance assessment. In this research, we propose a SMOLT software tool (sensitive mental workload enhanced with multi-task learning) for multidimensional workload assessment. SMOLT is innovative to build a multi-dimensional workload assessment model by incorporating advanced Multi-Task Learning (MTL) theory and multimodal deep learning, which is unique to model the relatedness among the output tasks (workload in different dimensions) and among input signals (multimodal deep learning for better feature representations). The proposed SMOLT is built on a significant amount of researches by our team to cognitive state assessment. We will incorporate the existing algorithms into SMOLT software and enhance the workload assessment in multi-dimensions.

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