Using NN for classification data from Accelerometer

[Murtowski]

Hello
I want to recognize gestures using accelerometer. After a couple of days I've reached the point where I received first results. But unfortunatelly they are rather poor.
I stick to the rules about building network that was described to some article about using NN to recognizing. (paper title: "High Accuracy Human Activity Monitoring using Neural Network")

        int networkInput = xAxis.size(); // about 100
        network = new BasicNetwork();
        //network.addLayer(new BasicLayer(null, true, networkInput));  
        network.addLayer(new BasicLayer(new ActivationTANH(), true, 3));
        network.addLayer(new BasicLayer(new ActivationTANH(), true, 7));
        network.addLayer(new BasicLayer(new ActivationLinear(), true, 3));
        network.getStructure().finalizeStructure();
        network.reset();

and training...

        final Propagation train = new ResilientPropagation(network, trainingSet);
        int epochsCount = 100;

        for(int epoch = 1; epoch > epochsCount; epoch++ ){
            train.iteration();
        }
        train.finishTraining();

How to use MLDataSet is so far for me one big unundersanding, but go further. I've tried two way of creating data; one is data[][] = {double[] xAxis, double[] yAxis, double[]zAxis} and second
data[][] = {double[] euclidesianOfThreeAxis} and look like the second works better.
Then I cannot use supervised trainging because I have only one sample , so i put null as ideal MLDataSet (Of course trainingSet is done before I train network^^).

       dataSet = new double[baseSize][];
        int i = 0;
        Iterator<Float> xIter = baseX.iterator();
        Iterator<Float> yIter = baseY.iterator();
        Iterator<Float> zIter = baseZ.iterator();
        while(xIter.hasNext()){
            dataSet[i] = new double[]{ Math.sqrt(Math.pow(xIter.next(),2) +
                    Math.pow(yIter.next(),2) + Math.pow(zIter.next(),2))};
            //dataSet[i] = new double[]{xIter.next(), yIter.next(), zIter.next()};
            i++;
        }
         NeuralDataSet trainingSet = new BasicNeuralDataSet(dataSet,null);

but for validation set I have to put 'IdealDataSet' as arg, otherweise compiler throw error, so I put twice the same dataSet.

         MLDataSet validationSet = new BasicMLDataSet(input,input);

The 'trainingSet' means for me the new gesture that I try to recognize among others. I save in memory' trainingSet' and 'network' as the constans that don't change over the recognizing process.
The 'validationSet' is created for each saved gesture for comparision with the 'trainingSet'.
I use function 'calculateError' to count similarity between trainingSet and validationSet.
But the results are very bad. That means algorithm make huge mistakes, where effectiveness (corectness) of error is under 25%.

          double error =  network.calculateError(validationSet) ;

The length of vectors of accelerations are the same, thats mean len(AxisX)=len(AxisY)=len(AxisZ)
and also len(savedGestures)=len(gestureToRecognize)

Can you help me and imporve my code? I will be so much thankfull for that.
I think the mistake is somewhere in the way that I put my vector to BasicMLDataSet or mayby in the way of training

The full code can be found here:
https://github.com/GitHubMurt/GestureRecognition/blob/newBranch/app/src/main/java/RecognitionTools/AlgNN.java