Early gesture recognition with adaptive window selection employing canonical correlation analysis for gaming

A new early gesture recognition system that uses different features obtained from MYO sensor is presented. The beginning part of each gesture is detected and used by the system to train the authors' recognition algorithm. To preserve the different features temporal alignment for each movement, two-dimensional (2D) principal component analysis was employed to obtain the dominant features by processing the obtained data in its 2D form. Canonical correlation analysis (CCA) is used to find a space where the projection of similar training testing pairs becomes highly correlated. Finally, the testing sequence is matched to the training set that gives maximum correlation in the new space obtained by CCA. Low processing complexity, storage requirement, accurate and fast decision obtained on the newly collected data set are factors that promotes the authors' algorithm for real-time implementation.