Spatialized audio improves call sign recognition during multi-aircraft control.

We investigated the impact of a spatialized audio display on response time, workload, and accuracy while monitoring auditory information for relevance. The human ability to differentiate sound direction implies that spatial audio may be used to encode information. Therefore, it is hypothesized that spatial audio cues can be applied to aid differentiation of critical versus noncritical verbal auditory information. We used a human performance model and a laboratory study involving 24 participants to examine the effect of applying a notional, automated parser to present audio in a particular ear depending on information relevance. Operator workload and performance were assessed while subjects listened for and responded to relevant audio cues associated with critical information among additional noncritical information. Encoding relevance through spatial location in a spatial audio display system--as opposed to monophonic, binaural presentation--significantly reduced response time and workload, particularly for noncritical information. Future auditory displays employing spatial cues to indicate relevance have the potential to reduce workload and improve operator performance in similar task domains. Furthermore, these displays have the potential to reduce the dependence of workload and performance on the number of audio cues.

Simulation-Based Evaluation of the Effects of Patient Load on Mental Workload of Healthcare Staff.

INTRODUCTION: In parts of Ohio, Veterans Affairs Medical Centers are working to handle patient load issues by sending patient overflows to the Wright-Patterson Medical Center. The Wright-Patterson Medical Center will benefit from the increase in patients; however, there are concerns that the patient quality of care may suffer. If the increase in patients results in the healthcare staff experiencing high mental workload levels, staff performance could be reduced. The objective of this research is to evaluate the influence of patient load on the mental workload of staff in an inpatient unit at the Wright-Patterson Medical Center. METHODS: This research uses discrete-event simulation to quantitatively model the mental workload of healthcare staff in an inpatient unit of the Wright-Patterson Medical Center. The model was used to find the idle time, average workload, and overload time of healthcare staff under current and future patient loads. In addition, the performance of individual tasks was evaluated. RESULTS: The results of this research find a linear relationship between patient load and three workload metrics (idle time, average workload, and overload time) with each worsening as patient load increases. Nurses and technicians experience the greatest negative impacts to mental workload as patient load increases with those staff members who have the most workload at the baseline condition experiencing greater increase in workload as patient load increases. In addition, the time spent in an overload state increases disproportionately with patient load increases, with overload time increases being worse for urgent tasks than for nonurgent tasks. CONCLUSIONS: Based on this study, the researchers found that the modeled inpatient unit can safely handle the expected patient load increases. The study provides the unit with information to proactively prepare and reduce healthcare staff overloading.

Assessing Continuous Operator Workload With a Hybrid Scaffolded Neuroergonomic Modeling Approach.

OBJECTIVE: We aimed to predict operator workload from neurological data using statistical learning methods to fit neurological-to-state-assessment models. BACKGROUND: Adaptive systems require real-time mental workload assessment to perform dynamic task allocations or operator augmentation as workload issues arise. Neuroergonomic measures have great potential for informing adaptive systems, and we combine these measures with models of task demand as well as information about critical events and performance to clarify the inherent ambiguity of interpretation. METHOD: We use machine learning algorithms on electroencephalogram (EEG) input to infer operator workload based upon Improved Performance Research Integration Tool workload model estimates. RESULTS: Cross-participant models predict workload of other participants, statistically distinguishing between 62% of the workload changes. Machine learning models trained from Monte Carlo resampled workload profiles can be used in place of deterministic workload profiles for cross-participant modeling without incurring a significant decrease in machine learning model performance, suggesting that stochastic models can be used when limited training data are available. CONCLUSION: We employed a novel temporary scaffold of simulation-generated workload profile truth data during the model-fitting process. A continuous workload profile serves as the target to train our statistical machine learning models. Once trained, the workload profile scaffolding is removed and the trained model is used directly on neurophysiological data in future operator state assessments. APPLICATION: These modeling techniques demonstrate how to use neuroergonomic methods to develop operator state assessments, which can be employed in adaptive systems.