  In this study we describe the development of a ride assistance application
which can be implemented on the widespread smart phones and tablet. The ride
assistance application has a signal processing and pattern classification
module which yield almost 100% recognition accuracy for real-time signal
pattern classification. We introduce a novel framework to build a training
dictionary with an overwhelming discriminating capacity which eliminates the
need of human intervention spotting the pattern on the training samples. We
verify the recognition accuracy of the proposed methodologies by providing the
results of another study in which the hand posture and gestures are tracked and
recognized for steering a robotic wheelchair.
