System for Incorporating Physiological Self-Regulation Challenge into Parcourse/Orienteering Type Games and Simulations

NASA’s biocybernetic system is a cutting-edge technology designed to cultivate emotional regulation skills. It leverages the concept of biocybernetic adaptation, where the trainee engages with virtual entities, such as characters in VR/AR/MR environments, whose behavior dynamically responds to the trainee's physiological signals. This responsive system provides real-time feedback, incentivizing the trainee to attain a calmer physiological state. The key components of this VR innovation include: · Head-mounted display hardware · Physiological monitoring hardware, tracking heart rate, breathing, sweat, breath, and brain waves · Software, powered by the Biocybernetic Loop (BL) Engine, integrating physiological data into the VR simulation · Character response avatars · Integration of the trainee's biofeedback data with the VR environment This technology relies on two functional elements working in unison to adapt the behavior and appearance of VR/AR/MR characters. Inference of the trainee's emotional state from physiological signals requires the implementation of advanced machine learning and modeling techniques. A pattern comparator stores templates of physiological patterns and continually assesses the proximity of the trainee's real-time physiological activity to the desired patterns. The pattern comparator calculates a closeness score in relation to one or more reference patterns, transmitting this data to the VR/AR/MR environment components. Consequently, the level of threat or cooperation presented by virtual characters is dynamically adjusted in response to the closeness score, creating an immersive and adaptive training experience.

The system uses perturbation feedback to help the athlete get into the zone through an original method of ZONE. The method allows a trainee to learn physiological self-regulation in order to modify the difficulty of the performance task and/or environment in which training is conducted. For example, better concentration leads to a variety of easier conditions on a training putting green. The technology incorporates software and hardware to provide real-time feedback to the athlete about how close his or her arousal and emotive responses are to an optimal state required to successfully perform the athletic task. This innovation presents the capability to extend current sports training and psychological practices of guided imagery visualization and cognitive reinforcement learning by systematically providing demonstrable and relevant feedback through the use of closed-loop, cybernetic feedback principles that provide immediate reinforcement of pyschophysiological self-regulation and translate into better skill-based performance.

In this method an autonomic entity manages a system through the generation of one or more stay alive signals by a hierarchical evolvable synthetic neural system. The generated signal is based on the current functioning status and operating state of the system and dictates whether the system will stay alive, initiate self-destruction, or initiate sleep mode. This method provides a solution to the long standing need for a synthetic autonomous entity capable of adapting itself to changing external environments and ceasing its own operation upon the occurrence of a predetermined condition deemed harmful.

This critical safety tool can be used for a wider variety of aircraft, including general aviation, helicopters, and unmanned aerial vehicles (UAVs) while also improving performance in the fighter aircraft currently using this type of system. Demonstrations/Testing This improved approach to ground collision avoidance has been demonstrated on both small UAVs and a Cirrus SR22 while running the technology on a mobile device. These tests were performed to the prove feasibility of the app-based implementation of this technology. The testing also characterized the flight dynamics of the avoidance maneuvers for each platform, evaluated collision avoidance protection, and analyzed nuisance potential (i.e., the tendency to issue false warnings when the pilot does not consider ground impact to be imminent). Armstrong's Work Toward an Automated Collision Avoidance System Controlled flight into terrain (CFIT) remains a leading cause of fatalities in aviation, resulting in roughly 100 deaths each year in the United States alone. Although warning systems have virtually eliminated CFIT for large commercial air carriers, the problem still remains for fighter aircraft, helicopters, and GAA. Innovations developed at NASAs Armstrong Flight Research Center are laying the foundation for a collision avoidance system that would automatically take control of an aircraft that is in danger of crashing into the ground and fly it—and the people inside—to safety. The technology relies on a navigation system to position the aircraft over a digital terrain elevation data base, algorithms to determine the potential and imminence of a collision, and an autopilot to avoid the potential collision. The system is designed not only to provide nuisance-free warnings to the pilot but also to take over when a pilot is disoriented or unable to control the aircraft. The payoff from implementing the system, designed to operate with minimal modifications on a variety of aircraft, including military jets, UAVs, and GAA, could be billions of dollars and hundreds of lives and aircraft saved. Furthermore, the technology has the potential to be applied beyond aviation and could be adapted for use in any vehicle that has to avoid a collision threat, including aerospace satellites, automobiles, scientific research vehicles, and marine charting systems.

Functional near-infrared spectroscopy (fNIRS) is an emerging hemodynamic neuroimaging brain-computer interface (BCI) technology that indirectly measures neuronal activity in the brain's cortex via neuro-vascular coupling. fNIRS works by quantifying hemoglobin-concentration changes in the brain based on optical intensity measurements, measuring the same hemodynamic changes as functional magnetic resonance imaging (fMRI). With enough probes in enough locations, fNIRS can detect these hemodynamic activations across the subject's entire head, thus allowing the determination of cognitive state through the use of pattern classification. fNIRS systems offer low-power, low-cost, highly mobile alternatives for real-time monitoring in safety-critical situations. Glenn's specific contribution to this field is the algorithms capable of removing motion artifacts (environment- or equipment-induced errors) from the device's head-worn optical sensors. In other words, Glenn's adaptive filter can determine the presence of a potential motion artifact based on a phase shift in the data measured; identify the artifact by examining the correlation between the phase shift and changes in hemoglobin concentration; and finally remove the artifact using Kalman filtering whenever changes in hemoglobin level and changes in the phase shift are not correlated. Glenn's breakthrough allows the advantages of fNIRS to be used for non-invasive real-time brain monitoring applications in motion-filled environments that could potentially save lives.