Analyzing Complex Systems Using an Integrated Multi-scale Systems Approach

Many industries, such as healthcare, transportation, education, and other fields that involve large corporations and institutions, are complex systems composed of many diverse interacting components. Frequently, to improve performance within these industries, to move into new markets, or to expand capability or capacity, decision-makers face opportunities or mandates to implement innovations (new technology, processes, and services). Successful implementation of these innovations involves seamless integration with the policy, economic, social, and technological dynamics associated with the complex system. These dynamics are frequently difficult for decision-makers to observe and understand. Consequently, they take on risk from lack of insight into how best to implement the innovation and how their system-of-interest will ultimately perform. This research defines a framework for an integrated, multi-scale modeling and simulation systems approach that provides decision-makers with prospective insight into the likely performance to expect once an innovation of change is implemented in a complex system. The need for such a framework when modeling complex systems is described, and suitable simulation paradigms and the challenges related to implementing these simulations are discussed. A healthcare case study is used to demonstrate the framework’s application and utility in understanding how an innovation, once fielded, will actually affect the larger complex system to which it belongs

A collaborative BCI approach to autonomous control of a prosthetic limb system

Existing brain-computer interface (BCI) control of highly dexterous robotic manipulators and prosthetic devices typically rely solely on neural decode algorithms to determine the user's intended motion. Although these approaches have made significant progress in the ability to control high degree of freedom (DOF) manipulators, the ability to perform activities of daily living (ADL) is still an ongoing research endeavor. In this paper, we describe a hybrid system that combines elements of autonomous robotic manipulation with neural decode algorithms to maneuver a highly dexterous robotic manipulator for a reach and grasp task. This system was demonstrated using a human patient with cortical micro-electrode arrays allowing the user to manipulate an object on a table and place it at a desired location. The preliminary results for this system are promising in that it demonstrates the potential to blend robotic control to perform lower level manipulation tasks with neural control that allows the user to focus on higher level tasks thereby reducing the cognitive load and increasing the success rate of performing ADL type activities