Anil Raj, MD
Anil Raj, MD
Florida Institute for Human & Machine Cognition
Research Scientist Anil Raj, M.D. (University of Michigan School of Medicine, 1990), followed his general surgery internship as a contract general medical officer with the US Army for one year. His interests in aerospace medicine research led him to the Naval Aerospace Medical Research Laboratory in Pensacola, Florida, following a two-year fellowship as a National Research Council Resident Research Associate at the NASA Johnson Space Center, in Houston, Texas. Dr. Raj’s interests focus around the human physiologic and psychological responses to external forces, particularly how they affect situation awareness. He has been involved with the development, test and evaluation phases of the US Navy and NASA’s Tactile Situation Awareness System (TSAS), and directed the first helicopter and first Unmanned Aerial Vehicle flight tests of TSAS. Since joining the Florida Institute for Human and Machine Cognition in 1996, Dr. Raj has been involved with the development of human-centered interfaces and the development of automated systems for tracking, analyzing, and manipulating human response characteristics in dynamic task environments. He led development of methods for integrating heterogeneous software agents with human-centered interfaces and adjustable autonomy as part of the DARPA Improving Warfighter Information Intake Under Stress (Augmented Cognition) program. He currently is working on machine learning systems for modeling brain function when using multisensory interfaces, investigating novel approaches to comparative effectiveness research, and developing sensory substitution approaches for augmenting situation awareness for able-bodied and disabled individuals.
Human Centered Design for Co-robotics
Human-robotic tasks would benefit from bidirectional interaction between the robot and the operator. The the application space drives the process of solution design. For a given environment, the process may include cognitive task analysis and system modeling to identify interaction bottlenecks between the human robotic system before ever attempting to implement a solution. This provides an opportunity to investigate the solution space before committing assets to hardware that is often missed. This guides the creation of a physical embodiment that does not introduce address the interaction bottlenecks. The final step requires validation of the solution through demonstration of changes in cognitive effort when utilizing the robotic systemt. We will discuss this multistep process in the context of surgical robotics and exoskeletons and other human machine systems.