Automation Technology and Human Performance: Current Research and Trends

By Mark W. Scerbo | Go to book overview

Dynamics of Supervisory Control Task Performance: SCAMPI Project Summary
Barrett S. Caldwell University of Wisconsin-Madison Madison, Wl
INTRODUCTION
The Supervisory Control Alertness Monitoring and Performance Indicators (SCAMPI) project was begun at the University of Wisconsin-Madison (UW) in early 1995. SCAMPI was developed in the context of a new UW research center bringing researchers from engineering, social, life, and medical sciences together to cooperate on improving human-system interactions. The UW Center for Human Performance in Complex Systems (CHPCS) was identified by both government and corporate participants as a unique opportunity for pursuing novel directions in technology developments and implementations to reduce human error and accidents in complex technological systems. One project specified by the Advanced Research Projects Agency (ARPA: now DARPA) was a need to create "adaptive automation" technologies which were capable of predicting and counteracting deficits in operator alertness while performing computer-based supervisory control tasks. Equipment and performance monitoring in adaptive automation is believed to be able to allow fewer human operators to interact more effectively with complex system processes.I was originally involved in SCAMPI development efforts on the basis of three relevant links to research conducted in my human factors group performance laboratory. The first link was based on my interests in information flow and methods to improve information presentation in human-machine interfaces (HMI) for cooperative task performance. The second link involved my development of quantitative approaches to analyzing human responses to changing cognitive task demands. These approaches are based on feedback control engineering systems tools using second-order differential equations of system responses to changing input functions. The third link incorporated research being conducted by a group at the Naval Research and Development (NRaD) Center in San Diego (including my graduate student Steven Murray), looking at human supervisory control of multiple autonomous robots, and real-time neural network algorithms for identifying operator state during task performance. By spring 1995, the SCAMPI project was tasked to demonstrate feasibility in three technology development areas:
Ambulatory, lightweight technologies to collect physiological data (including EEG and EKG) to be transmitted in "noisy" radio frequency environments;
Analysis tools to identify and predict short-term (minute scale) deficits in operator alertness and associated human performance decrements:
Strategies to incorporate negative feedback signals to improve the quality and reliability of human supervisory control HMI and adaptive automation.

Technologies to support wearable computing capabilities and ambulatory physiology data collection have already been in existence for several years, and continue to improve in performance and comfort ( Bass, 1998). Therefore, SCAMPI was not required to conduct specific technology development efforts in physiological data collection hardware. Similarly, neural science and physiological psychology researchers have focused on collecting data from specific brain and other sites that are thought to relate to human cognitive performance tasks ( Makeig, Elliott, & Posal, 1993; Wilson, Fullenkamp, & Davis, 1994). Thus, my tasks as leader of the SCAMPI project were to simply select candidate technologies capable of non-

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