Statistical analysis approach for posture recognition

The aim of this study is to determine the best eigenfeatures of four main human postures based on the rules of thumb of Principal Component Analysis namely the KG-rule, Cumulative Variance and the Scree Test followed by statistical analysis. Accordingly, all three rules of thumb suggest the retention of only 35 main principle components or eigenvalues. Next, these eigenfeatures that we named as dasiaeigenposturespsila are statistically analyzed prior to classification. Thus, the most relevant component of the selected eigenpostures can be ascertained. The statistical significance of the eigenpostures is determined using ANOVA. Further, a multiple comparison procedure (MCP) and homogeneous subsets tests are performed to determine the number of optimized eigenpostures for classification. artificial neural network (ANN) and support vector machine (SVM) were employed for classification. Results attained that the statistical analysis has enabled us to perform effectively the selection of eigenpostures for classification of human postures.