Make gaze smoothing and Kalman filter tuning configurable

src/index.mjs

@@ -262,7 +262,7 @@ async function getPrediction(regModelIndex) {
 /**
  * Runs every available animation frame if webgazer is not paused
  */
-var smoothingVals = new util.DataWindow(4);
+var smoothingVals = new util.DataWindow(webgazer.params.gazeSmoothingWindowSize);
 var k = 0;
 
 async function loop() {

src/params.mjs

@@ -21,6 +21,12 @@ const params = {
   dataTimestep: 50,
   showVideoPreview: true,
   applyKalmanFilter: true,
+  // Higher values reduce gaze-dot jitter but increase visual latency.
+  gazeSmoothingWindowSize: 8, // Previous value: 4
+  // Kalman tuning: higher measurement noise trusts raw gaze less; lower
+  // process noise makes movement smoother but slower to follow fast changes.
+  kalmanMeasurementNoise: 120, // Previous value: 47
+  kalmanProcessNoiseScale: 0.08, // Previous value: 1 / 10
   saveDataAcrossSessions: true,
   // Whether or not to store accuracy eigenValues, used by the calibration example file
   storingPoints: false,

src/ridgeRegThreaded.mjs

@@ -60,12 +60,11 @@ reg.RidgeRegThreaded.prototype.init = function() {
               [0,   1/4,  0,   1/2],    
               [1/2, 0,    1,   0],  
               [0,  1/2,  0,   1]];// * delta_t  
-    var delta_t = 1/10; // The amount of time between frames    
-    Q = mat.multScalar(Q, delta_t);    
+    Q = mat.multScalar(Q, params.kalmanProcessNoiseScale);    
 
     var H = [ [1, 0, 0, 0],
               [0, 1, 0, 0]];    
-    var pixel_error = 47; //We will need to fine tune this value [20200611 xk] I just put a random value here   
+    var pixel_error = params.kalmanMeasurementNoise;   
 
     //This matrix represents the expected measurement error 
     var R = mat.multScalar(mat.identity(2), pixel_error);  

src/util_regression.mjs

@@ -44,12 +44,11 @@ util_regression.InitRegression = function() {
     [0,   1/4,  0,   1/2],
     [1/2, 0,    1,   0],
     [0,  1/2,  0,   1]];// * delta_t
-  var delta_t = 1/10; // The amount of time between frames
-  Q = mat.multScalar(Q, delta_t);
+  Q = mat.multScalar(Q, params.kalmanProcessNoiseScale);
 
   var H = [ [1, 0, 0, 0],
     [0, 1, 0, 0]];
-  var pixel_error = 47; //We will need to fine tune this value [20200611 xk] I just put a random value here
+  var pixel_error = params.kalmanMeasurementNoise;
 
   //This matrix represents the expected measurement error
   var R = mat.multScalar(mat.identity(2), pixel_error);