internal static List <TrainingValue> GetTrainingValues(List <EMGPacket> packets, bool enableSkip)
{
List <TrainingValue> values = new List <TrainingValue>(EMGProcessor.FFT_SAMPLE_SIZE);
int skipsRemaining = 0;
for (int i = 0; i < packets.Count / EMGProcessor.FFT_SAMPLE_SIZE; i++)
{
if (enableSkip && skipsRemaining > 0)
{
skipsRemaining--;
continue;
}
Complex[] data = new Complex[EMGProcessor.FFT_SAMPLE_SIZE];
int start = i * EMGProcessor.FFT_SAMPLE_SIZE;
int end = start + EMGProcessor.FFT_SAMPLE_SIZE;
MuscleState startMuscleState = packets[start].muscleStateHint;
for (int j = start; j < end; j++)
{
EMGPacket packet = packets[j];
if (packet.muscleStateHint != startMuscleState)
{
skipsRemaining += EMGProcessor.SKIPS_AFTER_TRANSITION;
break;
}
data[j - start] = new Complex(EMGProcessor.ValueFromPacket(packet), 0);
}
if (enableSkip && skipsRemaining > 0)
{
continue;
}
FourierTransform.FFT(data, FourierTransform.Direction.Forward);
List <Complex> fftResults = new List <Complex>(data);
TrainingValue trainingValue = new TrainingValue((int)packets[start].muscleStateHint, EMGProcessor.FEATURE_COUNT);
EMGProcessor.FillTrainingValue(ref trainingValue, fftResults);
values.Add(trainingValue);
}
return(values);
}
private static void RunEMG()
{
EMGSerialReader reader = new EMGSerialReader("COM4", 1000);
EMGProcessor processor = new EMGProcessor(reader);
processor.Start();
long ticks = DateTime.Now.Ticks;
while (true)
{
//long dtTicks = DateTime.Now.Ticks - ticks;
//float dt = (float)dtTicks / 10000000;
processor.Update();
//ticks = DateTime.Now.Ticks;
Thread.Sleep(16);
}
}
