Paper Info
Title
A human workload assessment algorithm for collaborative human-machine teams
Abstract
Mass casualty events caused by a biological weapon require fully capable first response teams. However, human first responders are equipped with protective gear, which limits their capabilities to complete tasks. Robots can be employed to work collaboratively with the first responders in order to augment the human's reduced abilities. The robot needs to understand and adapt to the human's workload level in order for the human-machine team to effectively complete tasks. The automatic detection of human workload levels can provide valuable insight into the human's capabilities, as workload has a direct relationship with task performance. The robot can monitor the objective metrics of the human's workload level in order to accurately estimate workload via a workload assessment algorithm. The algorithm must be able to assess overall workload and the components of workload, in order for the robot to correctly adapt its interactions or reallocate tasks among the team. A novel workload assessment algorithm that provides an accurate estimate of overall workload and each workload component is presented and evaluated. The algorithm is capable of distinguishing between high and low workload conditions; however, the algorithm's workload values correlate poorly to a generated workload model. Modifications to enhance the algorithm's capabilities are discussed and will be investigated in future work.
Year
DOI
Venue
2017
10.1109/ROMAN.2017.8172328
2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)
Keywords
Field
DocType
human-robot interaction,task reallocation,automatic human workload level detection,human first responders,biological weapon,mass casualty events,collaborative human-machine team,workload model,high workload conditions,workload component,fully capable first response teams,human workload assessment algorithm,low workload conditions
Human–machine system,Algorithm design,Workload,Computer science,Visualization,Algorithm,Robot,Mass Casualty
Conference
ISSN
ISBN
Citations 
1944-9445
978-1-5386-3519-3
0
PageRank 
References 
Authors
0.34
4
3
Name
Order
Citations
PageRank
Jamison Heard110.72
Caroline E. Harriott2265.67
Julie A. Adams339253.75