ITR - (ASE + NHS) - (int): Intelligent Human-Machine Interface & Control for Highly Automated Chemical Screening Processes

North Carolina State University, Raleigh NC

Investigators

Abstract

High-throughput toxicity screening (testing) of dangerous chemical agents for effects on human cells and cell functions is a rapidly developing international, biotechnology industry. Advanced robots have replaced human operators and manual control of screening processes to promote safe, quick and accurate assessment of chemicals and toxins. In the screening process, the role of the human operator has changed to that of supervisory controller of multiple robots manning multiple process lines and performing simultaneous experiments. The operator's task now includes monitoring robot states, detecting errors, and intervening in process control for failure mode recovery. Operators are under high workload and time stress to properly sequence experiments and to quickly make changes if they do not progress as planned. The information requirements for effective performance have expanded dramatically; task workload is now primarily cognitive vs. physical; and there is a need to achieve high levels of situation awareness in dangerous chemical operations. All of these changes have had a major affect on operator stress and work health. In this project, the PI and his team will develop an intelligent/adaptive, human-machine interface to support the new role of screening process supervisors in safe and effective, distributed control of high time stress and high risk, automated chemical and toxicity testing. Development of this technology will be based on cognitive modeling of supervisory controller behaviors during actual chemical screening processes and model predictions of operator performance with different interactive information display design alternatives during the (model) design phase and during chemical process run-time. The PI will prototype control interfaces and shared situation awareness displays for operators that integrate and display process output data adapted to operator concurrent performance needs and functional (physiological) states. The work will also involve creating protocols for long-distance, remote operation of automated screening processes under varying network communication conditions to provide access to "start-up" companies and developing nations with critical screening needs (e.g., anti-terrorism work). In addition, the intelligent interface content and the remote process control scenario will be informed by, and adapted based on, an automated robot (mechanical) "health" monitoring system. Outcomes of the project will include a prototype novel intelligent, adaptive interface technology as embodied in a remote process control system. Advanced computational tools will also be developed to classify operator functional states in real-time, based on physiological data, and to relate this information to screening task workload and performance. Physiological and performance data models will be used as a basis for structuring the cognitive model to promote highly accurate operator performance predictions and facilitate effective dynamic interface configuration for remote screening process control. Broader Impacts: This research will impact and enhance the safety and effectiveness of high-throughput, chemical agent screening, thanks to a combination of new approaches to distributed network control of complex automated systems, user-centered design of adaptive/intelligent interfaces, and support of complex system operator situation awareness and decision making through process information integration and management based on operator functional states. The work will result in increased access for new companies and developing countries to highly specialized and expensive automated, chemical screening technologies potentially accelerating the development of new biotechnologies. In addition, the research will provide specialized training for graduate students participating in the project and through faculty development of new course modules, related to the project, integrated in existing computer science, electrical engineering, and industrial engineering curriculums.