Optimization of head movement recognition using augmented radial basis function neural network

Publication Type:
Conference Proceeding
33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 2776 - 2779
Issue Date:
Full metadata record
Files in This Item:
Filename Description SizeFormat
2011001915OK.pdf8.05 MBAdobe PDF
For people with severe spine injury, head movement recognition control has been proven to be one of the most convenient and intuitive ways to control a power wheelchair. While substantial research has been done in this area, the challenge to improve system reliability and accuracy remains due to the diversity in movement tendencies and the presence of movement artifacts. We propose a Neural- Network Configuration which we call Augmented Radial Basis Function Neural-Network (ARBF-NN). This network is constructed as a Radial Basis Function Neural-Network (RBF-NN) with a Multilayer Perceptron (MLP) augmentation layer to negate optimization limitation posed by linear classifiers in conventional RBF-NN. The RBF centroid is optimized through Regrouping Particle Swarm Optimization (RegPSO) seeded with K-Means. The trial results of ARBFNN on Head-movement show a significant improvement on recognition accuracy up to 98.1% in sensitivity.
Please use this identifier to cite or link to this item: