A Diagnostic Human Workload Assessment Algorithm for Collaborative and Supervisory Human--Robot Teams
Abstract
High-stress environments, such as first-response or a NASA control room, require optimal task performance, as a single mistake may cause monetary loss or even the loss of human life. Robots can partner with humans in a collaborative or supervisory paradigm to augment the human’s abilities and increase task performance. Such teaming paradigms require the robot to appropriately interact with the human without decreasing either’s task performance. Workload is related to task performance; thus, a robot may use a human’s workload state to modify its interactions with the human. Assessing the human’s workload state may also allow for dynamic task (re-)allocation, as a robot can predict whether a task may overload the human and, if so, allocate it elsewhere. A diagnostic workload assessment algorithm that accurately estimates workload using results from two evaluations, one peer based and one supervisory based, is presented. The algorithm correctly classified workload at least 90% of the time when trained on data from the same human--robot teaming paradigm. This algorithm is an initial step toward robots that can adapt their interactions and intelligently (re-)allocate tasks.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 03, 2019
- Source ID
- 10.1145/3314387
Entities
People
- Caroline E. Harriott
- Jamison Heard
- Julie A. Adams
- Rachel Heald
Organizations
- Air Force Office of Scientific Research
- National Aeronautics and Space Administration
- National Science Foundation
- Office of Naval Research
- Oregon State University
- University of Kansas
- Vanderbilt University