Title :
Performance prediction and limiting resource identification with nonlinear causal resource analysis
Author :
Kondraske, George V. ; Johnston, Charles ; Pearson, Alesia ; Tarbox, Layne
Author_Institution :
Human Performance Inst., Texas Univ., Arlington, TX, USA
fDate :
30 Oct-2 Nov 1997
Abstract :
Nonlinear Causal Resource Analysis (NCRA) is a relatively new method for task analysis and prediction of human performance based on the resource economic performance modeling constructs of General Systems Performance Theory (GSPT). NCRA was investigated in 30 subjects executing three mobility related tasks (gait, stair climbing, and obstacle course negotiation). A high degree of agreement (r=0.92, 0.95, and 0.96) was found with expert raters who used a visual analog scale to estimate performance of 30 subjects executing these tasks. As part of the NCRA prediction methodology, limiting performance resources are also identified (i.e., performance resources associated with lower level subsystems that limit execution of the higher level task to a specific level of performance). There is no other systematic method known for accomplishing this useful clinical objective; the limiting resources represent targets of therapy for pathologic subjects. Results add to the body of support for NCRA as an alternative to regression analysis for prediction as well as a tool for task analysis and also suggest utility for therapy planning
Keywords :
behavioural sciences; ergonomics; gait analysis; identification; physiological models; prediction theory; task analysis; economic performance modeling constructs; gait; general systems performance theory; human performance; limiting resource identification; lower level subsystems; mobility related tasks; nonlinear causal resource analysis; obstacle course negotiation; pathologic subjects; performance prediction; stair climbing; targets of therapy; task analysis; therapy planning; visual analog scale; Availability; Economic forecasting; Humans; Information analysis; Mathematical model; Medical treatment; Performance analysis; Predictive models; Regression analysis; System performance;
Conference_Titel :
Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-4262-3
DOI :
10.1109/IEMBS.1997.757081