Performance Risks During Surface Extravehicular Activity and Potential Mitigation Using Multimodal Displays.

BACKGROUND: Surface extravehicular activity (sEVA) will be a critical component of future human missions to the Moon. sEVA presents novel risks to astronaut crews not associated with microgravity operations due to fundamental differences in task demands, physiology, environment, and operations of working on the lunar surface. Multimodal spacesuit informatics displays have been proposed as a method of mitigating sEVA risk by increasing operator autonomy.METHODS: A formalized literature review was conducted. In total, 95 journal articles, conference papers, and technical reports were included. Characteristics of U.S. spacesuits were reviewed, ranging from the Apollo A7L to the xEMU Z-2.5. Multimodal display applications were then reviewed and assessed for their potential in aiding sEVA operations.RESULTS: Through literature review 25 performance impairments were identified. Performance impairments caused by the spacesuit represented the greatest number of sEVA challenges. Multimodal displays were mapped to impairments and approximately 36% of performance impairments could be aided by using display interfaces.DISCUSSION: Multimodal displays may provide additional benefits for alleviating performance impairments during sEVA. Utility of multimodal displays may be greater in certain performance impairment domains, such as spacesuit-related impairments.Zhang JY, Anderson AP. Performance risks during surface extravehicular activity and potential mitigation using multimodal displays. Aerosp Med Hum Perform. 2023; 94(1):34-41.

Estimation of Subjectively Reported Trust, Mental Workload, and Situation Awareness Using Unobtrusive Measures.

OBJECTIVE: We use a set of unobtrusive measures to estimate subjectively reported trust, mental workload, and situation awareness (henceforth "TWSA"). BACKGROUND: Subjective questionnaires are commonly used to assess human cognitive states. However, they are obtrusive and usually impractical to administer during operations. Measures derived from actions operators take while working (which we call "embedded measures") have been proposed as an unobtrusive way to obtain TWSA estimates. Embedded measures have not been systematically investigated for each of TWSA, which prevents their operational utility. METHODS: Fifteen participants completed twelve trials of spaceflight-relevant tasks while using a simulated autonomous system. Embedded measures of TWSA were obtained during each trial and participants completed TWSA questionnaires after each trial. Statistical models incorporating our embedded measures were fit with various formulations, interaction effects, and levels of personalization to understand their benefits and improve model accuracy. RESULTS: The stepwise algorithm for building statistical models usually included embedded measures, which frequently corresponded to an intuitive increase or decrease in reported TWSA. Embedded measures alone could not accurately capture an operator's cognitive state, but combining the measures with readily observable task information or information about participants' backgrounds enabled the models to achieve good descriptive fit and accurate prediction of TWSA. CONCLUSION: Statistical models leveraging embedded measures of TWSA can be used to accurately estimate responses on subjective questionnaires that measure TWSA. APPLICATION: Our systematic approach to investigating embedded measures and fitting models allows for cognitive state estimation without disrupting tasks when administering questionnaires would be impractical.

Patient-specific computational biomechanics of the brain without segmentation and meshing.

Motivated by patient-specific computational modelling in the context of image-guided brain surgery, we propose a new fuzzy mesh-free modelling framework. The method works directly on an unstructured cloud of points that do not form elements so that mesh generation is not required. Mechanical properties are assigned directly to each integration point based on fuzzy tissue classification membership functions without the need for image segmentation. Geometric integration is performed over an underlying uniform background grid. The verification example shows that, while requiring no hard segmentation and meshing, the proposed model gives, for all practical purposes, equivalent results to a finite element model.