This report covers three independent projects supporting this effort. Thus, the 2010 Neuroscience Director's Strategic Initiative (DSI) research aims to advance neuroscientific methodologies and technologies to enable revolutionary advances in Soldier-system performance by integrating modern neuroscience with human factors, psychology, and engineering.
In order to acquire, monitor, and assess Soldier sensory, perceptual, emotional, cognitive, and physical performance within realistic operational environments, new technologies must be further developed to enable sensing capabilities in these dynamic environments, and innovative research paradigms and data analysis methods must be enhanced, validated, and transitioned into system designs. Understanding how Soldiers' cognitive abilities meet the increasing demands of dynamic, complex, and stressful environments is critical for the development of systems that optimize mission effectiveness and maximize Soldier survivability. Our results demonstrate that we can indeed measure this impact, and furthermore measurements indicate that there are not universal differences in bar graphs and pie charts. In this paper, we use the classic comparison of bar graphs and pie charts to test the viability of fNIRS for measuring the impact of a visual design on the brain. It is unknown whether fNIRS can distinguish differences in cognitive state that derive from visual design alone. While functional near-infrared spectroscopy (fNIRS) has emerged as a practical brain sensing technology in HCI, visual tasks often rely on the brain's quick, massively parallel visual system, which may be inaccessible to this measurement. Unfortunately, objectively and unobtrusively monitoring the brain is difficult.
Research suggests that the evaluation of visual design benefits by going beyond performance measures or questionnaires to measurements of the user's cognitive state. We show how brain sensing can lend insight to the evaluation of visual interfaces and establish a role for fNIRS in visualization.
#Essential video analytics 6.30 full#
Video analytics, EEG, usability, auditory-evoked potentials, full motion video, Human Systems Integration, AVAA, cognit The study also developed and implemented a multiaspect approach to estimate operator functional state during system evaluation. The findings suggest that analysts were able to identify more targets with the V-NIIRS filter than in the baseline condition in time-pressured situations. Traditional subjective assessments of workload were augmented with continuous physiological and behavioral measurements in order to capture more accurate cognitive state fluctuations during human-system interaction. Measures of performance included percent of primary targets found, time to find primary target, total targets found, and buttons clicked. Experienced analysts searched for targets in full-motion video using AVAA software, both with and without V-NIIRS filter capabilities. This first-year assessment focused on the impact of V-NIIRS (Video National Imagery Interpretability Rating Scale), a widely used scale to evaluate video imagery quality. Human Systems Integration evaluation of the Advanced Video Activity Analytics (AVAA) system was conducted to capture baseline performance and workload with the AVAA system and compare it to performance with advanced AVAA features.
0 kommentar(er)
