@article{sheik-nainar_kaber_2007, title={The utility of a virtual reality locomotion interface for studying gait behavior}, volume={49}, ISSN={["0018-7208"]}, DOI={10.1518/001872007X215773}, abstractNote={ Objective: To investigate the effect of optic flow on gait behavior during treadmill walking using an immersive virtual reality (VR) setup and compare it with conventional treadmill walking (TW) and overground walking (OW). Background: Previous research comparing TW with OW speculated that a lack of optic flow (relative visual movement between a walker and the environment) during TW may have led to perceptual cue conflicts, resulting in differences in gait behavior, as compared with OW. Method: Participants walked under three locomotion conditions (OW, TW, and TW with VR [TWVR]) under three walking constraint conditions (no constraint, a temporal/pacing constraint, and a spatial/path-following constraint). Presence questionnaires (PQs) were administered at the close of the TWVR trials. Trials were subjected to video analysis to determine spatiotemporal and kinematics variables used for comparison of locomotion conditions. Results: ANOVA revealed gait behavior during TWVR to be between that of OW and TW. Speed and cadence during TWVR were significantly different from those of TW, whereas knee angle was comparable to that of OW. Correlation analysis of PQ scores with gait measures revealed a positive linear association of the distraction subfactor of the PQ with walking speed during TWVR, suggesting an increase in the sense of presence in the virtual environment led to increases in walking speed. Conclusion: The results demonstrate that providing optic flow during TW through VR has an impact on gait behavior. Application: This study provides a basis for developing simple VR locomotion interface setups for gait research. }, number={4}, journal={HUMAN FACTORS}, author={Sheik-Nainar, Mohamed A. and Kaber, David B.}, year={2007}, month={Aug}, pages={696–709} }
 @article{kaber_perry_segall_sheik-nainar_2007, title={Workload state classification with automation during simulated air traffic control}, volume={17}, ISSN={["1532-7108"]}, DOI={10.1080/10508410701527860}, abstractNote={Real-time operator workload assessment and state classification may be useful for decisions about when and how to dynamically apply automation to information processing functions in aviation systems. This research examined multiple cognitive workload measures, including secondary task performance and physiological (cardiac) measures, as inputs to a neural network for operator functional state classification during a simulated air traffic control (ATC) task. Twenty-five participants performed a low-fidelity simulation under manual control or 1 of 4 different forms of automation. Traffic volume was either low (3 aircraft) or high (7 aircraft). Participants also performed a secondary (gauge) monitoring task. Results demonstrated significant effects of traffic volume (workload) on aircraft clearances (p < .01) and trajectory conflicts (p < .01), secondary task performance (p < .01), and subjective ratings of task workload (p < .01). The form of ATC automation affected the number of aircraft collisions (p < .05), secondary task performance (p < .01), and heart rate (HR; p < .01). However, heart rate and heart rate variability measures were not sensitive to the traffic manipulation. Neural network models of controller workload (defined in terms of traffic volume) were developed using the secondary task performance and simple heart rate measure as inputs. The best workload classification accuracy using a genetic algorithm (across all forms of ATC automation) was 64%, comparable to prior work. Additional neural network models of workload for each mode of ATC automation revealed substantial variability in predictive accuracy, based on the characteristics of the automation. Secondary task performance was a highly sensitive indicator of ATC workload, whereas the heart rate measure appeared to operate as a more global indicator of workload. A limited range of cardiac response might be sufficient for the demands of the brain in ATC. The results have applicability to design of future adaptive systems integrating neural-network-based workload state classifiers for multiple forms of automation.}, number={4}, journal={INTERNATIONAL JOURNAL OF AVIATION PSYCHOLOGY}, author={Kaber, David B. and Perry, Carlene M. and Segall, Noa and Sheik-Nainar, Mohamed A.}, year={2007}, pages={371–390} }
 @article{kaber_wright_sheik-nainar_2006, title={Investigation of multi-modal interface features for adaptive automation of a human–robot system}, volume={64}, ISSN={1071-5819}, url={http://dx.doi.org/10.1016/j.ijhcs.2005.11.003}, DOI={10.1016/j.ijhcs.2005.11.003}, abstractNote={The objective of this research was to assess the effectiveness of using a multi-modal interface for adaptive automation (AA) of human control of a simulated telerobotic (remote-control, semi-autonomous robotic) system. We investigated the use of one or more sensory channels to cue dynamic control allocations to a human operator or computer, as part of AA, and to support operator system/situation awareness (SA) and performance. It was expected that complex auditory and visual cueing through system interfaces might address previously observed SA decrements due to unannounced or unexpected automation-state changes as part of adaptive system control. AA of the telerobot was based on a predetermined schedule of manual- and supervisory-control allocations occurring when operator workload changes were expected due to the stages of a teleoperation task. The task involved simulated underwater mine disposal and 32 participants were exposed to four types of cueing of task-phase and automation-state changes including icons, earcons, bi-modal (combined) cues and no cues at all. Fully automated control of the telerobot combined with human monitoring produced superior performance compared to completely manual system control and AA. Cueing, in general, led to better performance than none, but did not appear to completely eliminate temporary SA deficits due to changes in control and associated operator reorienting. Bi-modal cueing of dynamic automation-state changes was more supportive of SA than modal (single sensory channel) cueing. The use of icons and earcons appeared to produce no additional perceived workload in comparison no cueing. The results of this research may serve as an applicable guide for the design of human–computer interfaces for real telerobotic systems, including those used for military tactical operations, which support operator achievement and maintenance of SA and promote performance in using AA.}, number={6}, journal={International Journal of Human-Computer Studies}, publisher={Elsevier BV}, author={Kaber, David B. and Wright, Melanie C. and Sheik-Nainar, Mohamed A.}, year={2006}, month={Jun}, pages={527–540} }
 @article{sheik-nainar_kaber_chow_2005, title={Control gain adaptation in virtual reality mediated human-telerobot interaction}, volume={15}, ISSN={["1090-8471"]}, DOI={10.1002/hfm.20025}, abstractNote={The Internet connects millions of computers worldwide, and provides a new potential working environment for remote-controlled telerobotic systems. The main limitation of using the Internet in this application is random delays between communicating nodes, which can cause disturbances in human–machine interaction and affect telepresence experiences. This is particularly important in systems integrating virtual reality technology to present interfaces. Telepresence, or the sense of presence in a remote environment, hypothetically is positively related to teleoperation task performance. This research evaluated the effect of constant and random network (communication) delays on remote-controlled telerover performance, operator workload, and telepresence experiences. The research also assessed the effect of using a system gain adaptation algorithm to offset the negative impact of communication delays on the various response measures. It was expected that with gain adaptation, system stability, performance, and user telepresence experiences would improve with a corresponding decrease in workload. Results indicated that gain adaptation had a significant effect on the performance measures. The study demonstrated that gain adaptation could reduce deterioration in telepresence experiences and improve user performance in teleoperated and telerobotic control. © 2005 Wiley Periodicals, Inc. Hum Factors Man 15: 259–274, 2005.}, number={3}, journal={HUMAN FACTORS AND ERGONOMICS IN MANUFACTURING}, author={Sheik-Nainar, MA and Kaber, DB and Chow, MY}, year={2005}, pages={259–274} }