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Twelve Royal Marines participated in a laboratory study of a passive lower limb exoskeleton. Participants stood for 5 min in a semi-squatting posture in the laboratory while performing the Sustained Attention to Response Test (SART), repeated on separate days while wearing and without the exoskeleton. Performance on the SART was degraded when the semi-squatting posture was adopted and did not recover after 15 min of rest. The SART was rated as less frustrating when the exoskeleton was worn, while less time pressure and task conflict were reported and the need to exert self-control to maintain the posture was rated lower. There was no effect of the exoskeleton on SART performance. However, subjects' increase in heart rate when squatting, without the exoskeleton, was positively correlated with an increase in their SART error rate. The findings are discussed in relation to the cognitive demands of performing physically demanding tasks and the possibility that new technologies designed to enhance human physical ability may also improve cognitive capacity.

Situation Awareness (SA) can be impaired during mission operations due to excessive workload. This impairment may be especially noteworthy in pilots. Empirical data from the pilots were analyzed to find relationships between workload, SA, age, and flight-hours. Real-time SA measurements were made using the Situation Present Assessment Method (SPAM) for 22 volunteer test pilots who were flying along with a checking pilot (CP) from a traditional UH-1H helicopter fleet. In addition, before the post-flight debriefing, the researcher implemented the Situation Awareness Rating Technique (SART) and the NASA-TLX host-trial subjective self-rating to verify the SPAM data. Pilots' mental workload (MWL) data showed two highly negative correlations for pilots' ages and flight-hours. The SA data also showed two positive correlations for the pilots' ages and flight hours. Significant differences regarding pilots' ages were found for mental, physical, temporal, and effort workloads. The SA2 data showed a significant difference when the pilots correctly responded to questions and latencies on SA2, which proved that there is a significant difference according to the demands of concentration resources. There were also significant differences regarding the numbers of flight hours for the types of workloads, SA1-SA3 correct response numbers, and correct latencies, which proved there are significant differences for the demands on concentration resources and situation understanding. Multiple regression models also found age and flight hours can be used to predict pilots' MWLs. In particular, mental and temporal workloads can be predicted from high and low values of the flight hours variable. The findings make meaningful contributions to future mission scheduling and staff management.