Situation awareness measurement: A review of applicability for C4i environments

The construct of situation awareness (SA) has become a core theme within the human factors (HF) research community. Consequently, there have been numerous attempts to develop reliable and valid measures of SA but there is a lack of techniques developed specifically for the assessment of SA in command, control, communication, computers and intelligence (C4i) environments. During the design, development and evaluation of novel systems, technology and procedures, valid and reliable situation awareness measurement techniques are required for the assessment of individual and team SA, in order to determine the improvements (or in some cases decrements) resulting from proposed design and technological interventions. The paper presents a review of existing situation awareness measurement techniques for their suitability for use in the assessment of SA in C4i environments. Seventeen SA measures were evaluated against a set of HF methods criteria. It was concluded that current SA measurement techniques are inadequate by themselves for use in the assessment of SA in C4i environments, and a multiple-measure approach utilising different approaches is recommended

Video surveillance for monitoring driver's fatigue and distraction

Fatigue and distraction effects in drivers represent a great risk for road safety. For both types of driver behavior problems, image analysis of eyes, mouth and head movements gives valuable information. We present in this paper a system for monitoring fatigue and distraction in drivers by evaluating their performance using image processing. We extract visual features related to nod, yawn, eye closure and opening, and mouth movements to detect fatigue as well as to identify diversion of attention from the road. We achieve an average of 98.3% and 98.8% in terms of sensitivity and specificity for detection of driver's fatigue, and 97.3% and 99.2% for detection of driver's distraction when evaluating four video sequences with different drivers

Investigating eye movement acquisition and analysis technologies as a causal factor in differential prevalence of crossed and uncrossed fixation disparity during reading and dot scanning

Previous studies examining binocular coordination during reading have reported conflicting results in terms of the nature of disparity (e.g. Kliegl, Nuthmann, &amp; Engbert (Journal of Experimental Psychology General 135:12-35, 2006); Liversedge, White, Findlay, &amp; Rayner (Vision Research 46:2363-2374, 2006). One potential cause of this inconsistency is differences in acquisition devices and associated analysis technologies. We tested this by directly comparing binocular eye movement recordings made using SR Research EyeLink 1000 and the Fourward Technologies Inc. DPI binocular eye-tracking systems. Participants read sentences or scanned horizontal rows of dot strings; for each participant, half the data were recorded with the EyeLink, and the other half with the DPIs. The viewing conditions in both testing laboratories were set to be very similar. Monocular calibrations were used. The majority of fixations recorded using either system were aligned, although data from the EyeLink system showed greater disparity magnitudes. Critically, for unaligned fixations, the data from both systems showed a majority of uncrossed fixations. These results suggest that variability in previous reports of binocular fixation alignment is attributable to the specific viewing conditions associated with a particular experiment (variables such as luminance and viewing distance), rather than acquisition and analysis software and hardware.<br/

Towards a human eye behavior model by applying Data Mining Techniques on Gaze Information from IEC

In this paper, we firstly present what is Interactive Evolutionary Computation (IEC) and rapidly how we have combined this artificial intelligence technique with an eye-tracker for visual optimization. Next, in order to correctly parameterize our application, we present results from applying data mining techniques on gaze information coming from experiments conducted on about 80 human individuals

Assentication: User Deauthentication and Lunchtime Attack Mitigation with Seated Posture Biometric

Biometric techniques are often used as an extra security factor in authenticating human users. Numerous biometrics have been proposed and evaluated, each with its own set of benefits and pitfalls. Static biometrics (such as fingerprints) are geared for discrete operation, to identify users, which typically involves some user burden. Meanwhile, behavioral biometrics (such as keystroke dynamics) are well suited for continuous, and sometimes more unobtrusive, operation. One important application domain for biometrics is deauthentication, a means of quickly detecting absence of a previously authenticated user and immediately terminating that user's active secure sessions. Deauthentication is crucial for mitigating so called Lunchtime Attacks, whereby an insider adversary takes over (before any inactivity timeout kicks in) authenticated state of a careless user who walks away from her computer. Motivated primarily by the need for an unobtrusive and continuous biometric to support effective deauthentication, we introduce PoPa, a new hybrid biometric based on a human user's seated posture pattern. PoPa captures a unique combination of physiological and behavioral traits. We describe a low cost fully functioning prototype that involves an office chair instrumented with 16 tiny pressure sensors. We also explore (via user experiments) how PoPa can be used in a typical workplace to provide continuous authentication (and deauthentication) of users. We experimentally assess viability of PoPa in terms of uniqueness by collecting and evaluating posture patterns of a cohort of users. Results show that PoPa exhibits very low false positive, and even lower false negative, rates. In particular, users can be identified with, on average, 91.0% accuracy. Finally, we compare pros and cons of PoPa with those of several prominent biometric based deauthentication techniques