  Spotting signal patterns with varying lengths has been still an open problem
in the literature. In this study, we describe a signal pattern recognition
approach for continuous and simultaneous classification of a tracked hand's
posture and gestures and map them to steering commands for control of a robotic
wheelchair. The developed methodology not only affords 100\% recognition
accuracy on a streaming signal for continuous recognition, but also brings
about a new perspective for building a training dictionary which eliminates
human intervention to spot the gesture or postures on a training signal. In the
training phase we employ a state of art subspace clustering method to find the
most representative state samples. The recognition and training framework
reveal boundaries of the patterns on the streaming signal with a successive
decision tree structure intrinsically. We make use of the Collaborative ans
Block Sparse Representation based classification methods for continuous gesture
and posture recognition.
