Psychophysiological Monitoring of Aerospace Crew State
As next-generation space exploration missions necessitate increasingly autonomous systems, there is a critical need to better detect and anticipate astronaut interactions with these systems. The success of present and future autonomous technology in exploration spacecraft is ultimately dependent upon safe and efficient interaction with the human operator. Optimal interaction is particularly important for surface missions during highly coordinated extravehicular activity (EVA), which places high physical and cognitive demands on crew with limited ground support. Crew functional state may be affected by a number of variables including workload, stress, and motivation. Real-time assessments of crew state that do not require a crewmember’s time and attention to complete will be especially important to assess operational performance and behavioral health during flight. In response to the need for objective, passive assessment of crew state, the aim of this work is to develop an accurate and precise prediction model of human functional state for surface EVA using multi-modal psychophysiological sensing. The psychophysiological monitoring approach relies on extracting a set of features from physiological signals and using these features to classify an operator’s cognitive state. This work aims to compile a non-invasive sensor suite to collect physiological data in real-time. Training data during cognitive and more complex functional tasks will be used to develop a classifier to discriminate between high and low cognitive workload crew states. The classifier will then be tested in an operationally relevant EVA simulation to predict cognitive workload over time. Once a crew state is determined, further research into specific countermeasures, such as decision support systems, would be necessary to optimize the automation and improve crew state and operational performance.
Check out more in an article by Virginia Tech Daily and in the video below.
Within driver safety, I have researched crash severity metrics and driver behavior. For vehicle-based crash severity metrics, I evaluated the Occupant Load Criterion (OLC) and Acceleration Severity Index (ASI) as alternatives to the gold standard metric of vehicle change in velocity, or delta-v, for assessing crash severity. I completed this analysis using vehicle data from Event Data Recorders (EDRs) and occupant data from dummies in New Car Assessment Program (NCAP) crash tests. This work was presented at the 2017 Enhanced Safety of Vehicles (ESV) Conference.
In addition, I have studied driver-based crash severity metrics. Relaying occupant vital signs after a crash could improve emergency response by adding a direct measure of the occupant state to an Advanced Automatic Collision Notification (AACN) system. I tested the feasibility of using a seat sensor designed for occupant classification from a production passenger vehicle to measure an occupant’s respiration rate and heart rate in a laboratory setting. Using a technique known as ballistocardiography (BCG), this study aligned with my interests in non-invasive physiological monitoring. This work was published in Sensors in May 2018.
I have also worked with naturalistic driving data, specifically from the Strategic Highway Research Program 2 (SHRP2) dataset, to study age and gender differences in braking behavior. This research builds upon previous work looking at Time To Collision (TTC) in the 100-Car Naturalistic Driving Study.