A survey of workload assessment algorithms

Abstract

Supervisory control environments, such as the NASA control room can induce high workload levels in situations where a single error is capable of costing millions of dollars. An intelligent system can improve human supervisor performance by monitoring the human's workload levels and intelligently adapting the system capabilities, such as adapting the interaction medium or reallocating roles and responsibilities between the human and the system. Systems capable of responding promptly and accurately to the human's changes in workload require a workload assessment algorithm that can detect changes to all components of workload in real time. A review of 24 workload assessment algorithms across six task domains is provided. Each algorithm is reviewed based on four criteria: sensitivity, diagnosticity, suitability, and generalizability. The majority of the reviewed algorithms were developed for a specific task domain and are unable to generalize different tasks. Further, the majority of the algorithms do not account for individual differences, only assess one or two workload components, and do not classify underload.