Robust Gesture Recognition using a Prediction-Error-Classification Approach

The main idea of this paper consists on doing gesture recognition by means of prediction. Taking into account that a signal predictor will predict accurately future values of gestures of its class and inaccurately the values of the others, we can use the prediction error to classify the gestures. These predictors are implemented using neuro fuzzy systems. We call this approach prediction-error-classification approach (PEC) and this idea represents a different approach to solve the problem of gesture recognition in real time using inexpensive accelerometers. To validate this approach we have studied the impact of the number of training samples in the prediction error using cross-validation. We have also studied the impact of the number of training samples in the recognition rate, using again cross-validation. And to test the robustness and applicability in a real situation of this approach, we have repeated all the tests with a more realistic experiment.