## 5 Implementation and Evaluation
### HGR Performance Evaluation
#### 5.4.1 Recognition Accuracy  
Multilevel SVM RBF classifier measures the performance of the proposed model using accuracy as a metric, with selected \(k\) features fed as input from the feature selection algorithm. Figure 9a-d compares the performance of various feature selection algorithms with \(k\), for the lab, home, lab + home datasets with 276 gestures and lab dataset with 150 gestures respectively. The overall accuracy increases with an increase in \(k\) for MIFS and CIFE, which reports higher recognition accuracy than other feature selection algorithms. Based on the results, it is clearly visible that SVM achieves better recognition accuracy firstly with MIFS and secondly with the CIFE among all the tested feature selection algorithms. The performance of MIFS is evaluated with \(k=30\), 50 or 80 selected indices of cumulant features as input to the SVM classifier. Figure 10 shows the performance variation of MIFS with \(k\) features for different datasets for \(\beta=0.8\). With an increase in \(k\), the performance of MIFS increases in all environments. MIFS achieves better performance in the home environment for all \(k\) (30/50/80). The classification and recognition task reports better recognition accuracy for \(\beta=0.5\) and 0.8. MIFS achieves better recognition  
Figure 9: Comparison of feature selection algorithms with \(k\) (**a**) 276 gestures – lab dataset; (**b**) 276 gestures – home dataset; (**c**) 276 gestures – lab + home dataset; (**d**) 150 gestures – lab dataset. accuracy with \(k=80\), in all environments. Compared to the home environment, MIFS reports slightly lesser performance in a lab environment and further under performs in lab + home environment. In the lab environment, MIFS performance is better in a dataset with 276 gestures obtained from 1 user than the dataset with 150 gestures obtained from multiple users. For \(10\times 5\) cross-validation, MIFS performs better than all feature selection algorithms with an overall higher accuracy of 97.84%, 98.18% and 96.34% for lab, home, and lab + home environment respectively for 276 gestures with 8280 instances from one user. For 150 gestures with 7500 instances from five users in the lab environment MIFS achieves an overall higher accuracy of 96.23%. The recognition accuracy slightly declines for the lab data with 150 gestures as it involves multiple users for capturing the gesture instance, and the number of gesture samples per gesture per user decreases. HOS-Re achieves better recognition accuracy with \(\beta=0.8\) for \(k=80\) in all environments, as in Figure 10. Hence, to study the influence of \(\beta\) on the recognition accuracy with MIFS, experiments were carried out by systematically varying \(\beta\) from 0.1 to 1. Figure 11 highlights the variation in accuracy with \(\beta\) for different datasets. MIFS achieves better recognition accuracy with increasing \(\beta\) value as it reduces the uncertainty between the features and gesture class. Home dataset (276 gestures) shows higher accuracy than dataset from the lab (276 gestures). Lab + home showed the least accuracy levels compared to other datasets in 276 gestures category, for all \(\beta\). Lab and home dataset (276 gesture) show consistent accuracy levels at \(\beta=0.2\) and above. Interestingly, lab dataset shows a mildly decreasing trend in accuracy with an increase in \(\beta\). For Lab + home, the peak recognition accuracy of 96.34% observed at \(\beta=0.5\). MIFS captures the arbitrary mutual relations that exist between the individual third-order cumulant feature without performing any transformation. Therefore, MIFS provides the robust subset of cumulant features as the input to the multi-level SVM classifier preserving the mutual information with less uncertainty. Figure 11: Performance of MIFS with \(\beta\) on different datasets. Figure 10: Performance of MIFS with \(k\) features on a different dataset. Figure 12 shows the comparison of overall recognition accuracy of CIFE algorithm with \(k\) on different datasets.