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.