Techniques from the field of quality control can be used to classify the quality of individual samples of physical or cognitive performance. After stable baselines are established for an individual, deviations in performance can be evaluated using control charts. This paper addresses the development of effective analysis techniques for determining the presence of risk factors using performance-based measures. The proposed techniques capitalize on the within-session variability obtained by considering the multiple responses to stimuli within a session. The techniques were derived from statistical quality control and modified to fit the readiness-to-perform paradigm. Data collected from 10 participants, 174 trials, and 23 performance measures was analyzed using 18 variations of 3 different quality control charts. Additionally, the data represent performance under space microgravity or antihistamine conditions. The control chart techniques were evaluated in terms of their ability to diagnose the presence of risk factors while minimizing false alarms.
Abstract