# Discrete HMM for Visualizing Domiciliary Human Activity Perception and Comprehension
## 3 Experimental Results
### Experimental Evaluation  
Confusion Matrix Assessment  
The proposed work is validated by using the collected 275 samples of sequences with 16,172 frames for the five-fold cross-validation experiments, wherein cross-validation is a resampling method that uses different portions of the data to test and train a model on different iterations, treated as phases in terms of testing and training datasets, whereas the number of clusters in vector quantization is set as \(K=128\) and the number of states in HMMs is set as \(N=5\). The average accuracy reached 95.64%, wherein some discriminative poses, including standing, sitting, stop gesture, and follow-me, achieved 100% recognition accuracy. However, the walking, drinking, and follow-me poses could be easily misclassified, and therefore, they led to a lower recognition accuracy of 88%. To detail the misclassified rates for 11 poses in terms of the true classes with the corresponding predicted classes, a confusion matrix was derived, as shown in Table 1.  
In Table 1, by observing the drinking pose, in which 8% is misclassified as the calling-up pose. The aforementioned drinking pose is trained by left and right hands, respectively, such that the right-hand calling-up skeleton joints might be easily classified as the right-hand drinking skeleton joints, as shown in Figure 6(c2) of the drinking skeleton joints and Figure 6(d2) of the calling-up skeleton joints. In particular, 4% in the reading pose, 8% in the stretch pose, 4% in the summoning pose, and 8% in the walking pose, with the corresponding skeleton joints, as shown in Figure 6, are also partially misclassified as the drinking pose. The raised-hand pose has similar skeleton joint characteristics as the calling-up, the stretch, and the summoning poses, which leads to the misclassified rates, whereas the reading and walking poses give the perspective paradox on the misclassified rate, which is needed for further analysis. In addition, the akimbo pose is also partially misclassified in 4% as the follow-me pose. As an extension of the confusion matrix in Table 1, the derivative criteria, including the conditional positive, conditional negative, true positive, true negative, false positive, and false negative with \(F_{1}\) scores for the confusion matrix on the proposed 11 poses, are also examined and are shown in Table 2, where the \(F_{1}\) score measures the test's accuracy, by considering both the precision \(p\) and the recall \(r\) of the test to compute the score. The proposed D-HMMs with ground true data were validated in the previous section; then, a real-world validation was conducted by using ten participants to test the proposed work. Each participant performed 11 activities ten times in terms of differentiated acting speed and personal action style. Once the individual-person case was evaluated, the multiple-person case was further verified to entirely validate the robustness of the proposed work in the cases of single user and multiple users. The results are shown in Figure 7. In most cases, the classified rate of a single person is generally superior to the multiple person case; however, the drinking pose and the follow-me gesture in the multi-person case performed better classified rates than the single-person case, probably due to insufficient data.