ANN for Gesture Recognition using Accelerometer Data

This paper presents the application of Artificial Neural Networks to recognise among gestures trajectory patterns in a Euclidean space. The data was filtered and normalised by the Fast Fourier Transform. The k-means algorithm was used to parametrise the optimized data as input of the ANN by creating 15 clusters of data. Using the FANN tool, the ANN was modeled trained and tested so that the output of the ANN is the recognised gesture. The raw data comes from a set of 8 trajectories representing gestures captured by a device based on accelerometers like the Nintendo Wii remote.