Neural Networks to classify accelerometer double taps
Based on your post I'm assuming you haven't had much experience with this type of work. Here are several things that immediately jump out as being potential problems:


You should probably perform some type of preprocessing so your inputs are in a more acceptable range such as (-1, 1), or (0, 1). When the magnitude of the input values are large, the the hidden units are likely to saturate (i.e. be very close to $\pm$ 1 assuming you're using tanh hidden units). This causes the gradients to be nearly zero which can result in backpropagation performing poorly.
In my experience, you will not get good results when you have very unbalanced classes (50 tap, 3000 no tap) if you just naively apply some machine learning algorithm. For example, a classifier which always outputs -1 will achieve approximately 98% accuracy on your dataset, while being completely useless for your task.
Since you have not mentioned a training/test split of your dataset or cross validation, I'm assuming you're not doing these things. You should be. Neural networks are notorious for overfitting and if you use the same data to train and assess accuracy, you almost surely will.


That being said, I'm surprised this question hasn't been closed or migrated. It is almost certainly a better match for the CS stack exchange or Metaoptimize.