## 5 Results and Discussion  
To evaluate the classification performance of the proposed change point-based segmentation method, we conducted the performance comparison with the common used non-overlapping segmentation method and 50% overlapping segmentation method. The preprocessed dataset was divided into windows. Here, both non-overlapping segmentation method and 50% overlapping segmentation method used 2.56 s as the sliding window size. Afterwards, the features extracted from segmented windows are used as input to classifier. We investigated and compared the performance of various classifiers: SVM, KNN, DT, artificial neural network (MLP) and naive Bayes (NB), on three segmented datasets with the 10-fold cross-validation. To evaluate the performance of proposed ensemble model, we used the following metrics: (1) Accuracy: the probability that an activity is identified the same as the ground truth. (2) Sensitivity: the probability that actual positives are correctly identified. (3) Specificity: the probability that actual negatives that are correctly identified. (4) F-measure: the measure of a test's accuracy. The average accuracy of five classifiers on three segmented datasets were showed in Table 3, and the performance of DT was used as the baseline for comparison. The performance of RF contributed the highest accuracy of 94.21% on the segmented dataset obtained by using change detection-based segmentation, which significantly better than the baseline, even higher than the highest accuracy obtained from [12]. The classification results of MLP and NB were not good (72.21% and 63.45, respectively), even worse than the baseline. In terms of each segmented dataset, the average accuracy of change point-based segmentation method, 50% overlapping segmentation method and non-overlapping segmentation method by applying SVM classifier were 94.21%, 92.87% and 90.54%, respectively. Figure 2 depicted the sensitivity of each activity class obtained by applying SVM classifier on three segmented datasets. Generally, both non-overlapping segmentation method and 50% overlapping segmentation method performed well in identifying static activities and dynamic activities, while performed less well in identifying transitional activities, probably due to the durations of transitional activities were relatively short, the windows segmented by using non-overlapping segmentation method and 50% overlapping segmentation method contained more than one activities, resulting in misclassification of the transitional activities. The results indicated that change point-based segmentation method achieved better performance in terms of both overall classification accuracy and the specific classification accuracy of transitional activities. However, compared to static activities and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **DT** & **SVM** & **KNN** & **MLP** & **NB** \\ \hline Non-overlapping segmentation & 86.44 & 90.54 & 92.40 & 69.38 & 60.77 \\
50\% overlapping segmentation & 89.32 & 92.87 & 93.70 & 69.81 & 62.30 \\ Change point-based segmentation & 90.71 & 94.21 & 93.64 & 72.21 & 63.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation for the change point-based segmentation method. dynamic activities, change point-based segmentation method still had the relatively worse performance on identifying transitional activities. Thus, we adopted ensemble model with random feature subspace to enhance the performance on transitional activities classification in the next steps. To determine the best classification performance for each activity class, we analyzed the confusion matrices of each activities obtained from SVM, KNN and DT (showed in Figure 3). The confusion matrices revealed that SVM often misclassified walking class (83 instances), stand-to-sit (25 instances) and sit-to-stand (27 instances) to sweeping. Stand-to-walk class was frequently confused with walking (101 instances). Sit-to-lie class was misclassified to lie-to-sit (53 instances).