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Human Research Program

Optical Computer Recognition of Stress, Affect and Fatigue during Performance in Spaceflight

Completed Technology Project

Project Introduction

The two major laboratory experiments on the accuracy of OCR using a single camera were completed in the final funding period. One study involved measuring how well the single-camera OCR algorithm accurately tracked emotional expressions in healthy subjects who underwent emotional induction techniques. Preliminary analyses of the overall extent to which the initial 1-camera OCR algorithm could identify specific emotional expressions in many individuals with limited training revealed that the algorithm often failed to discriminate among emotions. That is, the algorithm had modest sensitivity and low specificity (i.e., it selected negative emotions too often and failed to discriminate among them). Although the facial expression models being used by the tracker were appropriate, it became apparent that a great deal of OCR inaccuracy was due to problems in identifying facial expressions when the face was partially out of view, which occurs frequently as people move their heads in all dimensional planes as they move about, work, etc. Thus, although we trained the OCR facial expression models with frontal images of facial expressions of emotion, the videos of subjects experiencing emotions would many times show subjects in non-frontal poses. The OCR algorithm model would then fail to correctly recognize the facial expression. To correct for this problem, the Metaxas Lab developed a sufficiently approximate warping transformation to warp the tracked face to a frontal pose (which is what the OCR algorithm expects to evaluate), as well as enhancing the algorithm with other analytic techniques that improve single-camera face tracking and extrapolation of facial expressions when the face is moving and/or partially out of view. The second validation experiment was conducted on a separate group of healthy adults randomized to either sleep deprivation or no sleep deprivation. The experiment sought to determine the extent to which the OCR algorithm detected ocular changes in slow eyelid closures (PERCLOS), and the extent to which the OCR PERCLOS measure reliably tracked lapses of attention during PVT performance. This was our first attempt to track PERCLOS with a 1-camera OCR algorithm. Subjects completed a 20-min PVT every 2 h while awake. Images of the face were recorded during each performance test. Coherence was calculated as the extent to which PVT lapses of performance were tracked by OCR-scored PERCLOS while subjects were and were not sleep deprived. The study revealed that the 1-camera OCR algorithm for PERCLOS had 73% sensitivity and 89% specificity for PVT performance lapses, thus confirming that the OCR PERCLOS detector rarely yielded false positives, and that it was acceptably high in sensitivity to fatigue-related performance risks in spaceflight. More »

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This is a historic project that was completed before the creation of TechPort on October 1, 2012. Available data has been included. This record may contain less data than currently active projects.

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