The objective of this research is to develop a trustworthy and high-performance neural-machine interface (NMI) that accurately determines a user?s locomotion mode in real-time for neural-controlled artificial legs. The proposed approach is to design the NMI by integrating a new pattern recognition strategy with a high-performance computing embedded system. This project tackles the challenges of accurate interpretation of information from the neuromuscular system, a physical system, using appropriate computation in a cyber system to process the information in real-time. The neural-machine interface consists of multiple sensors that reliably monitor the neural and mechanical information and a set of new algorithms that can fuse and coordinate the highly dynamic information for accurate identification of user intent. The algorithm is to be implemented on a high-performance graphic processing unit (GPU) to meet real-time requirements. This project has the potential to enable the design of neural-controlled artificial legs and may initiate a new direction for research in and the design of prosthetic leg systems. Innovations in this domain have the potential to improve the quality of life of leg amputees, including soldiers with limb amputations. The proposed approaches seek to permit cyber systems to cope with physical uncertainty and dynamics, a common challenge in cyber-physical systems, and to pave a way for applying high-performance computing in biomedical engineering. Besides providing comprehensive training to undergraduate and graduate students, the investigators plan to introduce community college students to cyber-physical systems concepts in an interactive and engaging manner.

The objective of this research is to establish a new development paradigm that enables the effective design, implementation, and certification of medical device cyber-physical systems. The approach is to pursue the following research directions: 1) to support medical device interconnectivity and interoperability with network-enabled control; 2) to apply coordination between medical devices to support emerging clinical scenarios; 3) to ?close the loop? and enable feedback about the condition of the patient to the devices delivering therapy; and 4) to assure safety and effectiveness of interoperating medical devices. The intellectual merits of the project are 1) foundations for rigorous development, which include formalization of clinical scenarios, operational procedures, and architectures of medical device systems, as well as patient and caregiver modeling; 2) high-confidence software development for medical device systems that includes the safe and effective composition of clinical scenarios and devices into a dynamically assembled system; 3) validation and certification of medical device cyber-physical systems; and 4) education of the next-generation of medical device system developers who must be literate in both computational and physical aspects of devices. The broader impacts of the project will be achieved in three ways. Novel design methods and certification techniques will significantly improve patient safety. The introduction of closed-loop scenarios into clinical practice will reduce the burden that caregivers are currently facing and will have the potential of reducing the overall costs of health care. Finally, the educational efforts and outreach activities will increase awareness of careers in the area of medical device systems and help attract women and under-represented minorities to the field.

The objective of this research is to integrate user control with automated reflexes in the human-machine interface. The approach, taking inspiration from biology, analyzes control-switching issues in brain-computer interfaces. A nonhuman primate will perform a manual task while movement- and touch-related brain signals are recorded. While a robotic hand replays the movements, electronic signals will be recorded from touch sensors on the robot?s fingers, then mapped to touch-based brain signals, and used to give the subject tactile sensation via direct cortical stimulation. Context-dependent transfers of authority between the subject and reflex-like controls will be developed based on relationships between sensor signals and command signals. Issues of mixed authority and context awareness have general applicability in human-machine systems. This research advances methods for providing tactile feedback from a remote manipulator, dividing control appropriate to human and machine capabilities, and transferring authority in a smooth, context-dependent manner. These principles are essential to any cyber-physical system requiring robustness in the face of uncertainty, control delays, or limited information flow. The resulting transformative methods of human-machine communication and control will have applications for robotics (space, underwater, military, rescue, surgery, assistive, prosthetic), haptics, biomechanics, and neuroscience. Underrepresented undergraduates will be recruited from competitive university programs at Arizona State University and Mexico's Tec de Monterrey University. Outreach projects will engage the public and underrepresented school-aged children through interactive lab tours, instructional modules, and public lectures on robotics, human-machine systems, and social and ethical implications of neuroprostheses.

The objective of this research is to study the formal design and verification of advanced vehicle dynamics control systems. The approach is to consider the vehicle-driver-road system as a cyber-physical system (CPS) by focusing on three critical components: (i) the tire-road interaction; (ii) the driver-vehicle interaction; and (iii) the controller design and validation. Methods for quantifying and estimating the uncertainty of the road friction coefficient by using self-powered wireless sensors embedded in the tire are developed for considering tire-road interaction. Tools for real-time identification of nominal driver behavior and uncertainty bounds by using in-vehicle cameras and body wireless sensors are developed for considering driver-vehicle interaction. A predictive hybrid supervisory control scheme will guarantee that the vehicle performs safely for all possible uncertainty levels. In particular, for controller design and validation, the CPS autonomy level is continuously adapted as a function of human and environment conditions and their uncertainty bounds quantified by considering tire-road and driver-vehicle interaction. High confidence is critical in all human operated and supervised cyber-physical systems. These include environmental monitoring, telesurgery, power networks, and any transportation CPS. When human and environment uncertainty bounds can be predicted, safety can be robustly guaranteed by a proper controller design and validation. This avoids lengthy and expensive trial and error design procedures and drastically increases their confidence level. Graduate, undergraduate and underrepresented engineering students benefit from this project through classroom instruction, involvement in the research and substantial interaction with industrial partners from the fields of tires, vehicle active safety, and wireless sensors.