public bool startRealtimeKNN(string id, string name)
{
EmoEngine engine = EmoEngine.Instance;
engine.UserAdded += new EmoEngine.UserAddedEventHandler(engine_UserAdded_Event);
engine.FacialExpressionEmoStateUpdated += new EmoEngine.FacialExpressionEmoStateUpdatedEventHandler(engine_FacialExpressionEmoStateUpdated);
engine.Connect();
EdkDll.IEE_DataChannel_t[] channelList = new EdkDll.IEE_DataChannel_t[7] {
EdkDll.IEE_DataChannel_t.IED_AF3, EdkDll.IEE_DataChannel_t.IED_AF4, EdkDll.IEE_DataChannel_t.IED_T7,
EdkDll.IEE_DataChannel_t.IED_T8, EdkDll.IEE_DataChannel_t.IED_O1, EdkDll.IEE_DataChannel_t.IED_GYROX, EdkDll.IEE_DataChannel_t.IED_GYROY
};
bool ready = false;
ready = knn.buildKNNCorpus(name);
IntPtr state = new IntPtr(1);
Thread newThread;
int itr = 0;
double[] alpha = new double[1];
double[] low_beta = new double[1];
double[] high_beta = new double[1];
double[] gamma = new double[1];
double[] theta = new double[1];
double[] data = new double[25];
while (ready)
{
itr++;
engine.ProcessEvents(10);
if (userID < 0)
{
continue;
}
for (int i = 0, j = 0; i < 5; i++)
{
engine.IEE_GetAverageBandPowers((uint)userID, channelList[i], theta, alpha, low_beta, high_beta, gamma);
data[j++] = theta[0];
data[j++] = alpha[0];
data[j++] = low_beta[0];
data[j++] = high_beta[0];
data[j++] = gamma[0];
}
if (data[0] != 0 && data[24] != 0)
{
newThread = new Thread(() => Form1.Instance.moveTestObject(knn.knnClassifier(data)));
newThread.Start();
}
Thread.Sleep(500);
}
return(ready);
}
public bool startRealtimeKNN(string id, string name)
{
EmoEngine engine = EmoEngine.Instance;
engine.UserAdded += new EmoEngine.UserAddedEventHandler(engine_UserAdded_Event);
engine.Connect();
EdkDll.IEE_DataChannel_t[] channelList = new EdkDll.IEE_DataChannel_t[5] {
EdkDll.IEE_DataChannel_t.IED_AF3, EdkDll.IEE_DataChannel_t.IED_AF4, EdkDll.IEE_DataChannel_t.IED_T7,
EdkDll.IEE_DataChannel_t.IED_T8, EdkDll.IEE_DataChannel_t.IED_O1
};
bool ready = false;
ready = knn.buildCorpusSimple(name);
IntPtr state = new IntPtr(1);
Thread newThread;
int itr = 0;
double[] alpha = new double[1];
double[] low_beta = new double[1];
double[] high_beta = new double[1];
double[] gamma = new double[1];
double[] theta = new double[1];
double[] data = new double[25];
while (ready)
{
itr++;
//if (EdkDll.IS_FacialExpressionIsLeftWink(state))
// MessageBox.Show("Left Eye Wink Detected!");
//if (EdkDll.IS_FacialExpressionIsRightWink(state))
// MessageBox.Show("Right Eye Wink Detected!");
engine.ProcessEvents(10);
if (userID < 0)
{
continue;
}
for (int i = 0, j = 0; i < 5; i++)
{
engine.IEE_GetAverageBandPowers((uint)userID, channelList[i], theta, alpha, low_beta, high_beta, gamma);
data[j++] = theta[0];
data[j++] = alpha[0];
data[j++] = low_beta[0];
data[j++] = high_beta[0];
data[j++] = gamma[0];
}
//up simple data taken form karrarUp.csv
//
//data = new double[] { 4.651620563, 5.212359266, 10.90409395, 5.157308481, 4.170539408, 5.10950104, 6.035994062, 12.41738389, 4.743290343, 3.866878481, 2.873242245, 4.77619567, 2.151664297, 1.760807141, 3.082304127, 3.986381183, 10.31494602, 9.029601134, 2.233667016, 2.991145428, 0.244647851, 0.618714049, 0.239897275, 0.36874583, 0.333496459 };
//data = new double[] { 2.254629381, 1.60659665, 1.328094785, 1.882520841, 1.027082428, 2.57410163, 2.209505082, 1.17652227, 0.9200791, 0.524185866, 2.047865372, 1.76506681, 1.091739745, 0.690766561, 0.820688245, 1.643871005, 5.476966707, 1.084435047, 1.055108772, 0.556177084, 335.9026309, 186.3244237, 140.337729, 67.65089061, 25.64029729 };
if (data[0] != 0 && data[24] != 0)
{
newThread = new Thread(() => Form1.Instance.moveObject(knn.knnClassifier(data)));
newThread.Start();
//newThread = new Thread(() => Form1.Instance.moveObject(knn.knnClassifier(pca.testDataPCA(data))));
}
Thread.Sleep(2000);
}
return(ready);
}
public double calculateKNNAccuracy()
{
if (fileExistance)
{
KNN knn = new KNN();
if (rightTestData != null)
{
knn.setRight(rightTrainData);
}
if (leftTestData != null)
{
knn.setLeft(leftTrainData);
}
if (upTestData != null)
{
knn.setUp(upTrainData);
}
if (downTestData != null)
{
knn.setDown(downTrainData);
}
if (neutralTestData != null)
{
knn.setNeutral(neutralTrainData);
}
double tp = 0, fp = 0;
if (rightTestData != null)
{
for (int i = 0; i < rightTestData.Length; i++)
{
if (knn.knnClassifier(rightTestData[i]) == "Right")
{
tp++;
}
else
{
fp++;
}
}
}
if (leftTestData != null)
{
for (int i = 0; i < leftTestData.Length; i++)
{
if (knn.knnClassifier(leftTestData[i]) == "Left")
{
tp++;
}
else
{
fp++;
}
}
}
if (upTestData != null)
{
for (int i = 0; i < upTestData.Length; i++)
{
if (knn.knnClassifier(upTestData[i]) == "Up")
{
tp++;
}
else
{
fp++;
}
}
}
if (downTestData != null)
{
for (int i = 0; i < downTestData.Length; i++)
{
if (knn.knnClassifier(downTestData[i]) == "Down")
{
tp++;
}
else
{
fp++;
}
}
}
if (neutralTestData != null)
{
for (int i = 0; i < neutralTestData.Length; i++)
{
if (knn.knnClassifier(neutralTestData[i]) == "Neutral")
{
tp++;
}
else
{
fp++;
}
}
}
double accuracy = (tp / (tp + fp)) * 100;
return(accuracy);
}
else
{
return(0);
}
}