Humans commanding and monitoring robots’ actions are used in various high-stress environments, such as the Predator or MQ-9 Reaper remotely piloted unmanned aerial vehicles. The presence of stress and potential costly mistakes in these environments places considerable demands and workload on the human supervisors, which can reduce task performance. Performance may be augmented by implementing an adaptive workload human–machine teaming system that is capable of adjusting based on a human’s workload state. Such a teaming system requires a human workload assessment algorithm capable of estimating workload along multiple dimensions. A multi-dimensional algorithm that estimates workload in a supervisory environment is presented. The algorithm performs well in emulated real-world environments and generalizes across similar workload conditions and populations. This algorithm is a critical component for developing an adaptive human–robot teaming system that can adapt its interactions and intelligently (re-)allocate tasks in dynamic domains.
Performing tasks quickly and accurately in dynamic and intense environments is critical, such as supervising a remotely piloted aircraft; however, these environments contain periods of low and high workload, which can decrease task performance. A system capable of intelligently adapting its interaction modality based on the human’s workload state may mitigate these undesirable workload states: underload and overload. Such a system requires mechanisms to determine accurately the human’s overall workload state and each workload component state (i.e., cognitive, physical, visual, speech, and auditory) in order to understand the current workload state’s underlying cause effectively. Existing work estimates multiple workload components, but no method estimates speech workload. This manuscript presents an algorithm for accurately estimating a human’s speech workload level using methods suitable for real-time workload assessment. The algorithm is an essential component to future adaptive human-machine interfaces.
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. CCS Concepts: • Computing methodologies → Neural networks; • Computer systems organization → Robotic autonomy;
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