Block-Sparse Representation Classification based gesture recognition approach for a robotic wheelchair

The Sparse Representation based Classification (SRC) method has been utilized for various pattern recognition problems, especially for face recognition. Upon its success, the SRC method is extended by introducing Block Sparsity (BS) for the signal to be recovered and much better results are reported in the related literature. In this study, we test three block sparsity approach: Block Sparse Bayesian Learning, Dynamic Group Sparsity and Block Sparse Convex Programming frameworks for the previously introduced SRC based gesture recognition algorithm. The results show that it yields faster and more accurate results than the SRC based gesture recognition algorithm and is suitable for real-time applications such as for commanding a robotic wheelchair.