System and methods for evaluating images and other subjects

Abstract

Eye movements and/or physiological characteristics of a user viewing a subject are detected and quantified to produce data to determine one or more details that is or was observable in the scene or image of which the user may or may not be consciously aware. Eye movement data includes the number of times a user looks at each area of a subject (“fixation number”), the length or duration of the fixation (“fixation duration value”), the number of times a user returns to look at a particular area (“fixation repeats”), and additionally any changes to pupil size based on dilation/constriction (“pupil size”). Physiological characteristic data includes heart rate, pulse or skin conductance. Advantageously, the system and methods may be used for a variety of purposes including evaluation improvement and training.

1 . A computer-implemented method comprising:
 displaying a subject on a display device for observation by a user;   overlaying a grid coordinate map over the subject, wherein a configuration of the grid coordinate map is determined based on at least a subset of features of the subject displayed on the display device;   generating eye movement data based on characteristics of at least one eye of the user;   identifying at least one area of interest on the subject based on the eye movement data generated; and   communicating the at least one area of interest identified.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein the eye movement data generated includes: a number of times the user looks at a particular area on the subject (“fixation number”), a time duration the user looks at the particular area on the subject (“fixation duration value”), a number of times the user returns to look at the particular area on the subject (“fixation repeats”), a pupil dilation/constriction associated with the user looking at the particular area on the subject (“pupil size”), or a combination thereof. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein the eye movement data generated includes a number of times the user looks at a particular area on the subject (“fixation number”) and wherein the generating includes:
 collecting a total number of points of gaze at each coordinate on the grid coordinate map to determine the fixation number; 
 determining whether the fixation number exceeds a threshold number; and 
 identifying the at least one area of interest in an event the fixation number determined exceeds the threshold number. 
 
     
     
         4 . The computer-implemented method of  claim 1 , wherein the eye movement data generated includes a time duration the user looks at a particular area on the subject (“fixation duration value”) and wherein the generating includes:
 measuring a duration of time of each point of gaze at each coordinate on the grid coordinate map to determine the fixation duration value; 
 determining whether the fixation duration value exceeds a threshold value; and 
 identifying the at least one area of interest in an event the fixation duration value exceeds the threshold value. 
 
     
     
         5 . The computer-implemented method of  claim 1 , wherein the eye movement data generated includes a number of times the user returns to look at a particular area on the subject (“fixation repeat number”) and wherein the generating includes:
 registering a point of gaze at a coordinate on the grid coordinate map; 
 registering an absence of the point of gaze at the coordinate on the grid coordinate map; 
 registering again the point of gaze at the coordinate on the grid coordinate map to provide a fixation repeat count; 
 determining the fixation repeat number based on the fixation repeat count; 
 determining whether the fixation repeat number exceeds a threshold number; and 
 identifying the at least one area of interest in an event the fixation repeat number exceeds the threshold number. 
 
     
     
         6 . The computer-implemented method of  claim 1 , wherein the generating includes:
 recording a first pupil diameter at a coordinate on the grid coordinate map at a first time;   recording a second pupil diameter at the coordinate on the grid coordinate map at a second time;   comparing the first pupil diameter recorded and the second pupil diameter recorded;   determining a dilation in an event the first pupil diameter recorded is less than the second pupil diameter recorded and determining a constriction in an event the first pupil diameter recorded is greater than the second pupil diameter recorded; and   identifying the at least one area of interest in an event the dilation is determined.   
     
     
         7 . The computer-implemented method of  claim 1 , wherein the communicating includes displaying metrics on the display device. 
     
     
         8 . The computer-implemented method of  claim 1 , further comprising:
 learning at least one pattern based on the eye movement data generated;   finding at least one deviation between the at least one pattern learned and at least one other pattern learned, the at least one other pattern learned based on other eye movement data; and   communicating, via the display device, the at least one deviation found.   
     
     
         9 . The computer-implemented method of  claim 8 , wherein the at least one area of interest includes an abnormality and wherein the at least one deviation represents a difference associated with detection of the abnormality by the user and at least one other user. 
     
     
         10 . The computer-implemented method of  claim 1 , further comprising determining the configuration and wherein determining the configuration includes:
 determining a spacing between two parallel vertical bars or between two parallel horizontal bars of the grid coordinate map based on the at least a subset of features.   
     
     
         11 . The computer-implemented method of  claim 1 , wherein the subset of features includes a level of illustration detail of the subject or a range of colors of the subject. 
     
     
         12 . The computer-implemented method of  claim 1 , wherein the configuration includes concentric circles spaced apart, randomly. 
     
     
         13 . The computer-implemented method of  claim 1 , wherein the grid coordinate map is not visible on the display device. 
     
     
         14 . A system comprising:
 a display device configured to display a subject for observation by a user;   an eye tracking apparatus configured to record characteristics of at least one eye of the user observing the subject; and   a processor component instructed to:
 overlay a grid coordinate map over the subject, wherein a configuration of the grid coordinate map is determined based on at least a subset of features of the subject displayed on the display device; 
 generate eye movement data based on the characteristics of the at least one eye of the user; 
 identify at least one area of interest on the subject based on the eye movement data generated; and 
 communicate the at least one area identified. 
   
     
     
         15 . The system of  claim 14 , wherein the eye movement data generated includes: a number of times the user looks at a particular area on the subject (“fixation number”), a time duration the user looks at the particular area on the subject (“fixation duration value”), a number of times the user returns to look at the particular area on the subject (“fixation repeats”), a pupil dilation/constriction associated with the user looking at the particular area on the subject (“pupil size”), or a combination thereof. 
     
     
         16 . The system of  claim 14 , wherein the eye movement data generated includes a number of times the user looks at a particular area on the subject (“fixation number”) and wherein the processor component is further instructed to:
 collect a total number of points of gaze at each coordinate on the grid coordinate map to provide the fixation number; 
 determine whether the fixation number exceeds a threshold number; and 
 identify the at least one area of interest in an event the fixation number determined exceeds the threshold number. 
 
     
     
         17 . The system of  claim 14 , wherein the eye movement data generated includes a time duration the user looks at a particular area on the subject (“fixation duration value”) and wherein the processor component is further instructed to:
 measure a duration of time of each point of gaze at each coordinate on the grid coordinate map to provide the fixation duration value; 
 determine whether the fixation duration value exceeds a threshold value; and 
 identify the at least one area of interest in an event the fixation duration value exceeds the threshold value. 
 
     
     
         18 . The system of  claim 14 , wherein the eye movement data generated includes a number of times the user returns to look at a particular area on the subject (“fixation repeat number”) and wherein the processor component is further instructed to:
 register a point of gaze at a coordinate on the grid coordinate map; 
 register an absence of the point of gaze at the coordinate on the grid coordinate map; 
 register again the point of gaze at the coordinate on the grid coordinate map to provide a fixation repeat count; 
 determine the fixation repeat number based on the fixation repeat count; 
 determine whether the fixation repeat number exceeds a threshold number; and 
 identify the at least one area of interest in an event the fixation repeat number exceeds the threshold number. 
 
     
     
         19 . The system of  claim 14 , wherein the processor component is further instructed to:
 record a first pupil diameter at a coordinate on the grid coordinate map at a first time;   record a second pupil diameter at a coordinate on the grid coordinate map at a second time;   compare the first pupil diameter recorded and the second pupil diameter recorded;   determine a dilation in an event the first pupil diameter recorded is less than the second pupil diameter recorded;   determine a constriction in an event the first pupil diameter recorded is greater than the second pupil diameter recorded; and   identify the at least one area of interest in an event the dilation is determined.   
     
     
         20 . The system of  claim 14 , wherein communicating the at least one area of interest identified includes displaying metrics on the display device. 
     
     
         21 . The system of  claim 14 , wherein the processor component is further instructed to:
 learn at least one pattern based on the eye movement data generated;   find at least one deviation between the at least one pattern learned and at least one other pattern learned, the at least one other pattern learned based on other eye movement data; and   communicate, via the display device, the at least one deviation found.   
     
     
         22 . The system of  claim 21 , wherein the at least one area of interest includes an abnormality and wherein the at least one deviation represents a difference associated with detection of the abnormality by the user and at least one other user. 
     
     
         23 . The system of  claim 14 , wherein the processor component is further instructed to:
 determine the configuration and wherein determining the configuration includes determining a spacing between two parallel vertical bars or between two parallel horizontal bars of the grid coordinate map based on the at least a subset of features.   
     
     
         24 . The system of  claim 14 , wherein the subset of features includes a level of illustration detail of the subject or a range of colors of the subject. 
     
     
         25 . The system of  claim 14 , wherein the configuration includes concentric circles spaced apart, randomly. 
     
     
         26 . The system of  claim 14 , wherein the grid coordinate map is not visible on the display device.