Thumb Motion Classification using Discrimination Functions

This paper deals with the classification of three dimensional thumb motions. The thumb motion is parameterized tracing a special mark on the thumb, the coordinates of thumb trajectory are computed during the parameterization. The discrimination functions used for the classification of thumb motion are derived in this paper. The discrimination functions are based on statistical data analysis. The functions are derived from the Bayes theorem. The normal distribution of motion parameters is assumed. The parameters of discrimination functions are estimated using the Maximum Likelihood Estimation. The presented classification is used in biomedical engineering for research on correlations between a human brain function and a muscle activity.