## 3 Experimental Results and Discussion
### Evaluation of the Two Unobserved Video Subsequences with Random Lengths Scenarios  
In this section, we evaluate the performance of the proposed approach in recognizing the four fall-related activities from partially-observed video sequences with two unobserved frames subsequences. In particular, we consider two configurations for the locations of the two unobserved frames subsequences. In the first configuration, we construct partially-observed video sequences by generating two temporal gaps with random lengths at the beginning and the end of the testing video sequences. In the second configuration, we generate two temporal gaps with random lengths between the beginning and the end of the testing video sequences. Next we discuss the evaluation results of our proposed approach for the aforementioned two configurations. 3.1 Evaluation Results When the Two Temporal Gaps Are at the Beginning and the End of the Video Sequences  
In this evaluation scenario, we aim at evaluating the performance of the proposed approach in recognizing the four fall-related activities when the unobserved video subsequences are at the beginning and the end of the video sequence. To achieve that, we created two temporal gaps. The first temporal gap spans a subset of video frames that belong to the first and second sub-activities; while the second gap spans video frames that belong to the second and last sub-activities. Hence, the majority of the remaining frames belong to the second sub-activity. To achieve this goal, we have set the intervals of the two temporal gaps to \([1,\beta_{1}]\) and \([\beta_{2},T]\), where \(\beta_{1}\) and \(\beta_{2}\) are two random integers in the ranges of \(1<\beta_{1}<\frac{4T}{10}\) and \(\frac{6T}{10}<\beta_{2}<T\), respectively. Table 7 presents the recognition results of the four \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Activity** & **Precision** & **Recall** & **F1-Measure** & **Average Gap’s Length (Frames)** \\ \hline Walking & 86.0\% & 72.7\% & 78.8\% & 21 \\ Sitting & 76.2\% & 85.0\% & 80.4\% & 27 \\ Falling from sitting & 82.1\% & 84.0\% & 83.0\% & 24 \\ Falling from standing & 73.2\% & 82.0\% & 77.3\% & 22 \\ \hline Overall average & 79.4\% & 80.9\% & 79.9\% & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The recognition results of the four fall-related activities from partially-observed videos when the missing subsequence of frames is at the middle of the testing video sequences. fall-related activities expressed in terms of the precision, recall and F1-measure as an evaluation metric. The last two columns in Table 7 provide the average lengths of the two temporal gaps introduced in the testing video sequences of each activity over the 10 repetitions of the LOVSO-CV procedure. The proposed approach achieved an average recognition accuracy of 58.8% in identifying the four fall-related activities from partially-observed video sequences. Table 7 shows that the recognition results have been reduced drastically compared with the results of the previous evaluation scenarios. This reduction in the recognition accuracy is due to the large amount of unobserved video frames, which are mainly frames from the first and the last sub-activities of each activity. These sub-activities have a key role in distinguishing between different fall-related activities, such as sitting and falling from sitting activities. 3.2 Evaluation Results When the Two Temporal Gaps Are between the Beginning and the End of the Video Sequences  
In this evaluation scenario, we aim at evaluating the performance of the proposed approach in recognizing the four fall-related activities; the unobserved video subsequences are at random locations between the beginning and the end of the video sequence, which is similar to the scenario described in Figure 1c. To achieve that, we created two temporal gaps. The first gap spans a subset of the video frames that belong to the first and second sub-activities. While the second gap spans a subset of the video frames that belong to the second and last sub-activities.