Menu
(2) BMed03:What are the optimal methods to detect and assess decrements in behavioral health (which may negatively affect performance) during exploration missions?
Goel, Namni ( University of Pennsylvania )
In Experiment 1 (emotion recognition), 15 hours of footage for facial emotional analysis was collected. Subjective emotional questionnaires also were administered to all subjects.
In Experiment 2 (stress and fatigue detection), 180 hours of digitally recorded high definition footage was collected capturing the faces of subjects during performance of the Psychomotor Vigilance Task (PVT). Facelab data and PVT reaction times were simultaneously recorded throughout these test bouts, which were administered every 2 hours over the course of two consecutive days. 3 hours of footage was recorded for facial emotional analysis and 20 hours of footage also was collected for stress analysis. In addition, data from neuropsychological tasks, personality questionnaires, and subjective emotional rating scales were obtained from all subjects.
With regard to the stress-related hormone analysis in Experiment 2, a total of 45 saliva samples were collected during stress-inducing tasks (5 saliva samples per subject). EEG/EKG data were also collected during these stress-inducing tasks.
NASA Human Research Program Investigators' Workshop, Abstract Book, February 2010. , Feb-2010
Aviation, Space, and Environmental Medicine. 2009 Mar;80(3):224. , Mar-2009
NASA Human Research Program Investigators' Workshop, Abstract Book, February 2009. , Feb-2009
(2) BMed03:What are the optimal methods to detect and assess decrements in behavioral health (which may negatively affect performance) during exploration missions?
Metaxas, Dimitri ( Rutgers University )
The project has four specific aims: (1) Create an OCR system capable of monitoring facial displays of specific emotions (i.e. angry, happy and sad). (2) Improve our current OCR system's ability to detect facial expressions of high versus low performance-induced stress. (3) Develop OCR algorithms to identify fatigue due to sleep loss based on slow eyelid closures (PERCLOS). (4) Test the technical feasibility of data acquisition and reliability of the advanced OCR system in spaceflight analogs that contain neurobehavioral stressors relevant to spaceflight (e.g., NEEMO). The project has primary relevance to strategic goals of the NSBRI Neurobehavioral and Psychosocial Factors (NBPF) Team. It addresses a high priority gap identified by the NASA SAT, BHP, and NSBRI NBPF area, and specifically targets questions 25d,c,g,h of Bioastronautics Roadmap Risk Area 25 (Human Performance Failure Due to Neurobehavioral Problems), and question 27d in Risk Area 27 (Human Performance Failure Due to Sleep Loss and Circadian Rhythm Problems).
We have made several other new developments to the OCR system: (1) the technique was validated with the use only one camera, where the previous method required two; (2) we improved tracking by using a manifold of faces that helped automatically track the face as the head moves; (3) we added the use of Conditional Random Fields in addition to Hidden Markov Modeling, to the algorithm which improved its computational efficiency; and (4) GABOR filtering (used for edge detection in image analysis) was incorporated into the ASM algorithm to track changes in facial texture, allowing it to identify features (e.g., furrowed brow).
In the first year of the current project we have begun expanding the algorithm to recognize facial expressions of emotion and behavioral indicators of excessive sleepiness (through slow eyelid closures). We are also continuing our work to improve the system's ability to correctly identify stress. Preliminary data confirm that the experimental procedures reliably induce stress, emotion and fatigue. In this first year we designed and implemented the two experiments we proposed (one on emotion detection and one on stress and fatigue detection. Twenty healthy subjects have completed the two experiments (N=9 in Experiment 1 and N=11 in Experiment 2). We are using these data to expand and improve the current OCR algorithm.
(2) BMed03:What are the optimal methods to detect and assess decrements in behavioral health (which may negatively affect performance) during exploration missions?
Metaxas, Dimitri ( Rutgers University )
This optical computer recognition (OCR) system will provide feedback to them for autonomous selection of countermeasures for stress, depression and fatigue. The project will be accomplished through collaborative efforts of Dr. David Dinges (Unit for Experimental Psychiatry) at the University of Pennsylvania School of Medicine, and Dr. Dimitris Metaxas (Computational Biomedicine Imaging and Modeling Center) at Rutgers University.
Specific Aims
1) Create an OCR system capable of monitoring facial displays of specific emotions (i.e., angry, happy and sad).
2) Improve our current OCR systems ability to detect facial expressions of high-performance versus low-performance-induced stress.
3) Develop OCR algorithms to identify fatigue due to sleep loss based on slow eyelid closures.
4) Test the technical feasibility of data acquisition and reliability of the advanced OCR system in spaceflight analogs, such as NEEMO, that contain neurobehavioral stressors relevant to spaceflight.
The project has primary relevance to strategic goals of the NSBRI Neurobehavioral and Psychosocial Factors (NBPF) Team. It addresses a high-priority gap identified by the NASA Small Assessment Team, Behavioral Health and Performance, and NSBRI NBPF Team areas. and the project specifically targets questions 25d, c, f, and h of Bioastronautics Roadmap Risk Area 25 (Human Performance Failure Due to Neurobehavioral Problems), and question 27d in Risk Area 27 (Human Performance Failure Due to Sleep Loss and Circadian Rhythm Problems).
Developed and operated by: NASA Research and Education Support ServicesNASA Application Owner: Greg Lindsay
NASA Privacy, Disclaimer and Accessibility Statement