filterEyeTrackInfant

 Kalman filter missing data from eye track
 Samuel Rivera and Fabian Benitez for Kalman Filtering
 June 24, 2011
 Notes: This function filters eye tracks to smooth and eliminate missing
       data

 Syntax: eyeTrack = filterEyeTrackInfant( eyeTrack )

 Inputs: 
       eyeTrack: (Nx1) vector  of complex (x+i*y) coordinates.  -1-i1  to identify
           missing data.  The paramters used here work well for data
           scaled between [0 1]
 
 Outputs:
      eyeTrack: the kalman filtered version

0001 % Kalman filter missing data from eye track
0002 % Samuel Rivera and Fabian Benitez for Kalman Filtering
0003 % June 24, 2011
0004 % Notes: This function filters eye tracks to smooth and eliminate missing
0005 %       data
0006 %
0007 % Syntax: eyeTrack = filterEyeTrackInfant( eyeTrack )
0008 %
0009 % Inputs:
0010 %       eyeTrack: (Nx1) vector  of complex (x+i*y) coordinates.  -1-i1  to identify
0011 %           missing data.  The paramters used here work well for data
0012 %           scaled between [0 1]
0013 %
0014 % Outputs:
0015 %      eyeTrack: the kalman filtered version
0016 
0017 function eyeTrack = filterEyeTrackInfant( eyeTrack, smoothParam )
0018            
0019 % oriEyes = eyeTrack;
0020 
0021 %---------------------------------------
0022 %filter using Kalman
0023 
0024 eyeTrack = [real(eyeTrack) imag(eyeTrack) ];
0025 eyeTrack( eyeTrack == -1) = nan;
0026 
0027 t = ones( 1, length( eyeTrack ) );
0028 t( isnan( eyeTrack(:,1) )) = -1; %mising is -1, filled is 1
0029 
0030 if nargin < 2 || isempty( smoothParam)
0031     smoothParam = .0003;  %  to increase smoothness, lower the number
0032                       % 1000 empirically works on the up-scaled eye track data
0033                       % in [.0001 , .0005] empirically works on the data in [ 0 1 ]
0034 end
0035 
0036 known = find( t == 1); 
0037 while(known(2)-known(1) >1) %make sure not a single thing, so we can calculate things
0038     known(1) = [];
0039 end
0040 
0041 % Identify the large gaps of missing eye tracks
0042 % don't interpolate the large missing gaps
0043 missingWindow = 4;
0044 interpThese = ones( 1, length( eyeTrack ) );
0045 for i1 =missingWindow:length(interpThese)    
0046     i1_check = i1 - [missingWindow-1:-1:0];
0047     if sum( t(i1_check) == -1) == missingWindow
0048         interpThese( i1_check ) = 0;
0049     end
0050 end    
0051 % find start/end of the gaps to interpolate
0052 k1 = 1;
0053 inABlock = 0;
0054 for i1 = 1:length(interpThese)    
0055     if (inABlock==0) && (interpThese(i1) ==1)
0056         startInterp(k1) = i1;
0057         inABlock = 1;
0058     end
0059     if (inABlock==1) && (interpThese(i1) ==0)
0060         endInterp(k1) = i1-1;
0061         inABlock = 0;
0062         k1 = k1+1;
0063     end
0064     % make sure to end at last sample if started already
0065     if (i1 ==length(interpThese)) && (inABlock==1)
0066         endInterp(k1) = i1;
0067     end    
0068 end
0069 
0070 % now interpolate just within those blocks of uninterrupted track
0071 for i1 = 1:length( startInterp )
0072     [eyeTrack1]=kalmanMissingData( eyeTrack(startInterp(i1):endInterp(i1),:)', t(startInterp(i1):endInterp(i1)), smoothParam)';
0073     eyeTrack(startInterp(i1):endInterp(i1),:) = eyeTrack1;
0074 end
0075 
0076 eyeTrack(isnan(eyeTrack)) = -1;
0077 eyeTrack = eyeTrack(:,1)+1i*eyeTrack(:,2);
0078 
0079