## 3 Experimental Results and Discussion
### Evaluation of the Two Unobserved Video Subsequences with Random Lengths Scenarios
Hence, the remaining frames are sparsely distributed between the first, second and last sub-activities. To implement the temporal gaps in this evaluation scenario, we have set the intervals of the two temporal gaps to \([\beta_{1},\beta_{2}]\) and \([\gamma_{1},\gamma_{2}]\), where \(\beta_{1}\), \(\beta_{2}\), \(\gamma_{1}\) and \(\gamma_{2}\) are random integers that satisfy the two conditions: \(\frac{2T}{10}<\beta_{1}<\beta_{2}<\frac{4T}{10}\) and \(\frac{6T}{10}<\gamma_{1}<\gamma_{2}<\frac{8T}{10}\). Table 8 provides the recognition results of the four fall-related activities expressed in terms of the precision, recall and F1-measure as an evaluation metrics. The last two columns in Table 8 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 71.4% in identifying the four fall-related activities from partially-observed video sequences. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Activity**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1-Measure**} & \multicolumn{2}{c}{**Average Length of**} & \multicolumn{2}{c}{**Average Length of**} \\  & & & & **the First Temporal** & **the Second Temporal** \\  & & & & **Gap (Frames)** & **Gap (Frames)** \\ \hline Walking & 63.3\% & 52.7\% & 57.5\% & 18 & 16 \\ Sitting & 64.4\% & 62.0\% & 68.7\% & 22 & 27 \\ Falling from sitting & 57.0\% & 66.0\% & 61.1\% & 20 & 18 \\ Falling from standing & 54.2\% & 64.0\% & 58.7\% & 17 & 19 \\ \hline Overall average & 59.7\% & 61.2\% & 61.5\% & 19 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Recognition results of the fall-related activities from partially-observed videos with missing video subsequences at the beginning (first temporal gap) and the end (second temporal gap) of the video sequences. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Activity**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1-Measure**} & \multicolumn{2}{c}{**Average Length of**} & \multicolumn{2}{c}{**Average Length of**} \\  & & & & **the First Temporal** & **the Second Temporal** \\  & & & & **Gap (Frames)** & **Gap (Frames)** \\ \hline Walking & 78.3\% & 73.6\% & 75.9\% & 10 & 9 \\ Sitting & 73.1\% & 72.0\% & 72.5\% & 11 & 12 \\ Falling from sitting & 71.6\% & 76.3\% & 73.9\% & 13 & 10 \\ Falling from standing & 72.2\% & 78.1\% & 75.0\% & 12 & 11 \\ \hline Overall average & 73.8\% & 75.0\% & 74.3\% & 11 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Recognition results of the four fall-related activities from partially-observed videos with two unobserved video subsequences between the beginning and the end of each testing video sequence. The results in Table 8 show that, in this evaluation scenario, the proposed approach achieved a better recognition accuracy compared with the results obtained when the two temporal gaps were at the beginning and the end of the testing video sequences, as described in Section 3.3.1. This can be justified by observing that each testing video consists of three observed video subsequences after creating the temporal gaps. The first observed video subsequence is at the beginning of the testing video and consists of video frames from the first sub-activity. The second observed video subsequence is at the middle of the testing video and might contain frames from more than one sub-activity depending on the lengths of the temporal gaps. The third observed video subsequence is at the end of the testing video and consists of video frames from the last sub-activity. This implies that the observed video subsequences are comprising video frames from the different sub-activities of an activity in a given testing video, which can enhance the recognition accuracy.