Publication statistics
Pub. period:1995-2011
Pub. count:5
Number of co-authors:16
Co-authors
Number of publications with 3 favourite co-authors:
Daniel N. Cassenti:4Debra J. Patton:1Jennifer C. Swoboda:1 Productive colleagues
Troy D. Kelley's 3 most productive colleagues in number of publications:
Laurel Allender:7Herbert A. Colle:5Daniel N. Cassenti:4
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Troy D. Kelley
Publications by Troy D. Kelley (bibliography)
Cassenti, Daniel N., Kelley, Troy D., Avery, Erin and Yagoda, Rosemarie E. (2011): Location Label Speech Options Improve Robot Operator Performance. In: Proceedings of the Human Factors and Ergonomics Society 55th Annual Meeting 2011. pp. 439-443.
“Operators increasingly use speech communication to direct robots. Thus, research on how robot operators may most effectively use speech communication is increasingly important. If certain types of speech communication increase performance then it is incumbent on robot designers to produce robots that allow for these types of communication. An experiment was conducted as a follow up to a previous experiment (Cassenti, Kelley, Swoboda,&Patton, 2009) to test whether participants who were given the ability to use certain location labels performed better than those who could only give direction commands (i.e., turn right, move forward). The results indicated that robot performance was improved when participants could direct a robot using location labels. We interpret these results to suggest that robots which are designed to perform indoor navigation have improved performance when participants can use location labels for structural parts of a building (i.e., doors, halls, and rooms with numerical labels). We recommend that the robotic platform used in the present study be developed to recognize these location labels by incorporating visual recognition algorithms and map incorporation skills.”
© All rights reserved Cassenti et al. and/or HFES
Cassenti, Daniel N., Kelley, Troy D., Colle, Herbert A. and McGregor, Elizabeth A. (2011): Modeling Performance Measures and Self-Ratings of Workload in a Visual Scanning Task. In: Proceedings of the Human Factors and Ergonomics Society 55th Annual Meeting 2011. pp. 870-874.
“Mental workload is the amount of demand on an individual's limited mental resources and thus is an important consideration in human factors research. This research focuses on workload from two primary methods of measuring it -- self-ratings of workload and performance. An experiment to test workload involved the manipulation of the number of tasks to be performed at once and the time available to respond to the task or tasks. The results show that performance changes by shifting between ceiling, linear decrease, and floor performance as workload increases. SWAT ratings of workload followed the same pattern. We conclude that the IMPRINT (Improved Performance Research Integration Tool; Archer&Adkins, 1999) modeling system should maintain its existing method of modeling self ratings of workload, but that they may make use of a new algorithm based on this data to model performance as workload changes.”
© All rights reserved Cassenti et al. and/or HFES
Cassenti, Daniel N., Kelley, Troy D. and Carlson, Richard A. (2010): Modeling the Workload-Performance Relationship. In: Proceedings of the Human Factors and Ergonomics Society 54th Annual Meeting 2010. pp. 1684-1688.
“Human factors research is often focused on the mental workload that is required to perform a task or set of tasks with the goal of reducing workload to make systems easier to manage. The Improved Performance Research Integration Tool (IMPRINT) includes an algorithm to predict mental workload. The algorithm was developed using subject matter expert ratings of workload tasks. We aimed to enhance this capability by developing algorithms using data from four new studies investigating change in performance as demands on mental resources increase. The results indicate three task types of similar difficulty and one task type of much greater difficulty. We then map these to our hypothesized workload function. Finally, we propose a way forward in modeling performance as a function of workload in IMPRINT.”
© All rights reserved Cassenti et al. and/or HFES
Cassenti, Daniel N., Kelley, Troy D., Swoboda, Jennifer C. and Patton, Debra J. (2009): The Effects of Communication Style on Robot Navigation Performance. In: Proceedings of the Human Factors and Ergonomics Society 53rd Annual Meeting 2009. pp. 359-363.
“With the advance of computer speech recognition programs, robotic operators have a new method of robot-operator communication. An experiment was conducted using a "Wizard of Oz" paradigm to investigate how different styles of communication affect robot navigation performance. Using manual inputs, verbal commands (restricted to directions only), and verbal commands with object referent labels, participants navigated a simulated robot through various simulated indoor environments. Results indicated that manual control was faster than free form verbal commands but not faster than simple directional commands. When provided the opportunity, participants did use object labels particularly objects related to the structure of the building (doors, rooms, and halls). Discussion focuses on improving robotic communication and object recognition in a robotic control system.”
© All rights reserved Cassenti et al. and/or their publisher
Allender, Laurel, Kelley, Troy D., Salvi, Lucia, Lockett, John, Headley, Donald B., Promisel, David, Mitchell, Diane, Richer, Celine and Feng, Theo (1995): Verification, Validation, and Accreditation of a Soldier-System Modeling Tool. In: Proceedings of the Human Factors and Ergonomics Society 39th Annual Meeting 1995. pp. 1219-1223.
“Increasingly, system developers are relying on modeling and simulation to support early design decisions. In turn, to support effective, timely use of models and simulations, verification, validation, and, in some cases, accreditation (VV&A) are required. The soldier-system analysis tools collectively known as Hardware vs. Manpower (HARDMAN) III underwent a formal VV&A process, the first of its type in the Army. The first phase comprised the core task network modeling capability and the effects implemented as additions to or modifications of the task data-mental workload estimation and environmental degradation, personnel characteristics, and training. A review board of representative users, policy-makers, technical experts, and soldier proponents evaluated the findings against eight criteria -- configuration management, software verification, documentation, data input requirements, model granularity, validity of modeling techniques and embedded algorithms, output, and analysis timelines. All criteria were satisfied and formal accreditation was granted with only limited caveats.”
© All rights reserved Allender et al. and/or Human Factors Society
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