## 3 Experimental Results and Discussion
### Evaluation on the Single Unobserved Video Subsequence with Random Length Scenarios
#### 3.2.1 Evaluation Results When the Temporal Gap Is at the Beginning 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 subsequence is happening at the beginning of the video sequence. In particular, we have investigated the scenario in which the temporal gap is mainly affecting the video frames belonging to the first sub-activity and a subset of the second sub-activity in each of the four fall-related activities. Hence, our assumption is that the majority of the observed video frames belong to the last sub-activity. To achieve this goal, we have set the temporal gap interval to \([1:\lambda]\), where \(\lambda\) is a random integer that is larger than one and less than 60% of the total length of the video sequence. Table 4 shows the results of recognizing the four fall-related activities expressed in terms of the precision, recall and F1-measure as an evaluation metric. In addition, Table 4 provides the average length of the temporal gaps introduced in the testing video sequences for each activity over the 10 repetitions of the LOVSO-CV procedure. The proposed approach achieved an average recognition accuracy of 81.3% in identifying the four fall-related activities from partially-observed video sequences.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Activity** & **Precision** & **Recall** & **F1-Measure** & **Average Gap’s Length (Frames)** \\ \hline Walking & 90.8\% & 81.5\% & 85.9\% & 32 \\ Sitting & 84.0\% & 85.0\% & 84.5\% & 37 \\ Falling from sitting & 76.2\% & 79.0\% & 77.6\% & 34 \\ Falling from standing & 77.0\% & 78.4\% & 77.7\% & 38 \\ \hline Overall average & 82.0\% & 81.0\% & 81.4\% & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The recognition results of the four fall-related activities from partially-observed videos when the missing subsequence of frames is at the beginning of the testing video sequences.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Activity** & **Precision** & **Recall** & **F1-Measure** \\ \hline Walking & 95.0\% & 94.4\% & 94.7\% \\ Sitting & 92.1\% & 90.1\% & 91.1\% \\ Falling from sitting & 97.0\% & 96.5\% & 96.7\% \\ Falling from standing & 96.1\% & 95.3\% & 95.7\% \\ \hline Overall average & 95.1\% & 92.8\% & 94.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The recognition results of the four fall-related activities from fully-observed video sequences.  
The results in Table 4 indicate that the average recognition accuracy has been reduced to 81.3% compared to 93.6% that was obtained in the fully-observed scenario. This can be attributed to the fact that the ratio of the average length of the introduced temporal gaps across the four activities in this scenario, which is equal to 35 frames, to the average sequence length in our dataset, which is equal to 84 frames, is approximately 42%. The relatively high lengths of the introduced temporal gaps can generate unobserved subsequences that represent multiple sub-activities, which in turn reduces the recognition accuracy. For example, when the unobserved video subsequence is spanning the first two sub-activities in the falling from sitting and falling from standing activities, the remaining sub-activity represents the falling pose, which is common to both activities. This can reduce the ability of the proposed approach to distinguish between different activities that involve fall-related events.