DETECTION OF OPERATOR PERFORMANCE BREAKDOWN IN A MULTITASK ENVIRONMENT.
Degree: PhD, Industrial Engineering, 2015, Purdue University
The purpose of this dissertation work is: 1) to empirically demonstrate an extreme human operator’s state, performance breakdown (PB), and 2) to develop an objective method for detecting such a state. PB has been anecdotally described as a state where the human operator “loses control of the context” and “cannot maintain the required task performance.” Preventing such a decline in performance could be important to assure the safety and reliability of human-integrated systems, and therefore PB could be useful as a point at which automation can be applied to support human performance. However, PB has never been scientifically defined or empirically demonstrated. Moreover, there exists no method for detecting such a state or the transition to that state. Therefore, after symbolically defining PB, an objective method of potentially identifying PB is proposed. Next, three human-in-the-loop studies were conducted to empirically demonstrate PB and to evaluate the proposed PB detection method. Study 1 was conducted: 1) to demonstrate PB by increasing workload until the subject reports being in a state of PB, and 2) to identify possible parameters of the PB detection method for objectively identifying the subjectively-reported PB point, and determine if they are idiosyncratic. In the experiment, fifteen participants were asked to manage three concurrent tasks (one primary and two secondary tasks) for 18 minutes. The primary task’s difficulty was manipulated over time to induce PB while the secondary tasks’ difficulty remained static. Data on participants’ task performance was collected. Three hypotheses were constructed: 1) increasing workload will induce subjectively-identified PB, 2) there exists criteria that identify the threshold parameters that best detect the performance characteristics that maps to the subjectively-identified PB point, and 3) the criteria for choosing the threshold parameters are consistent across individuals. The results show that increasing workload can induce subjectively-identified PB, although it might not be generalizable — 12 out of 15 participants declared PB. The PB detection method was applied on the performance data and the results showed PB can be identified using the method, particularly when the values of the parameters for the detection method were calibrated individually. Next, study 2 was conducted: 1) to repeat the demonstration of inducing PB, 2) to evaluate whether the threshold parameters established in study 1 for the PB detection method can be used in a subsequent study, or whether they have to be re-calibrated for each study, and 3) to examine whether a specific physiological measure (pulse rate) can be used to identify the subjectively-reported PB point. Study 2 was conducted in the same task environment (three concurrent tasks) as study 1. Three hypotheses were constructed: 1) increasing workload will induce subjectively-identified performance breakdown, 2) the threshold parameters established from study 2 will be the same as those from study 1 for all participants and will…
Advisors/Committee Members: Steven J Landry, Paul U Lee, Barrett Caldwell, Ji Soo Yi.
Subjects/Keywords: Adaptive Automation; Automation Triggering Mechanism; Dynamic Function Allocation; Human Factors; Human Performance; Human Performance Breakdown
to Zotero / EndNote / Reference
APA (6th Edition):
Yoo, H. (2015). DETECTION OF OPERATOR PERFORMANCE BREAKDOWN IN A MULTITASK ENVIRONMENT. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/1161
Chicago Manual of Style (16th Edition):
Yoo, Hyo-Sang. “DETECTION OF OPERATOR PERFORMANCE BREAKDOWN IN A MULTITASK ENVIRONMENT.” 2015. Doctoral Dissertation, Purdue University. Accessed November 14, 2019.
MLA Handbook (7th Edition):
Yoo, Hyo-Sang. “DETECTION OF OPERATOR PERFORMANCE BREAKDOWN IN A MULTITASK ENVIRONMENT.” 2015. Web. 14 Nov 2019.
Yoo H. DETECTION OF OPERATOR PERFORMANCE BREAKDOWN IN A MULTITASK ENVIRONMENT. [Internet] [Doctoral dissertation]. Purdue University; 2015. [cited 2019 Nov 14].
Available from: https://docs.lib.purdue.edu/open_access_dissertations/1161.
Council of Science Editors:
Yoo H. DETECTION OF OPERATOR PERFORMANCE BREAKDOWN IN A MULTITASK ENVIRONMENT. [Doctoral Dissertation]. Purdue University; 2015. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1161