## 4 Experiments
### Implementation and Results  
In the experimental work, step-by-step procedures for object detection were conducted. Then, the acquired silhouetted objects were used to extract useful features through _VGP_. After that, the extracted features from the human body were analyzed to detect falls. At this point, we stress the importance of using step-by-step methodologies of statistical analysis for precisely detecting abnormal and normal events. We first calculated the moving average (_MA_) by observing details of human behavior and posture. For estimating the possibility of an abnormality through the crossing point, we performed approval calculations for the modified difference (_MD_), including its local maximum (\(l_{max}\)), and local minimum (\(l_{min}\)). In addition, the period of the falling event was analyzed using the half width value (\(v_{lmw}\)). In order to classify events as abnormal or normal, the \(l_{max}\) and \(v_{lmw}\) of aspect ratio (\(r\)), \(l_{min}\) and \(v_{lmw}\) of point distance (\(d\)) were used as the input features into a support vector machine (_SVM_). To visualize the input features, Figures 11 and 12, respectively, illustrate \(r\) and \(d\) conducting the linear discriminating line for classification. Then, period detection (_PD_) using the half width value was performed to confirm falls. In addition, some of the experimental results for distinguishing abnormal from normal events are illustrated in Figures 13-15, respectively. The analytical results for fall trajectories were demonstrated as shown in Figure 16.  
Figure 11: Results for abnormal and normal event classification using aspect ratio. (**a**) Event classification based on local maximum (\(l_{max}\)) and \(v_{lmw}\). (**b**) Linear discrimination for class categories.  
Figure 12: Results for abnormal and normal event classification using point distance. (**a**) Event classification based on local minimum (\(l_{min}\)) and \(v_{lmw}\). (**b**) Linear discrimination for class categories.  
Figure 14: Analyzing abnormal and normal events in scenario 2. (**a**) Abnormal event analysis based on \(l_{max}\). (**b**) Abnormal event analysis based on \(l_{min}\).  
Figure 15: Analyzing abnormal and normal events in scenario 3. (**a**) Abnormal event analysis based on \(l_{max}\). (**b**) Abnormal event analysis based on \(l_{min}\).  
Figure 13: Analyzing abnormal and normal events in scenario 1. (**a**) Abnormal event analysis based on \(l_{max}\). (**b**) Abnormal event analysis based on \(l_{min}\).  
For Figure 16, the following explains fall scenarios obtained from the trajectories.  
* Scenario 1: In the video scene, the person is walking from the right side to the chair near the window. Then, the person turns back and immediately falls down.
* Scenarios 2, 3 and 8: In the video scenes, the person immediately falls down while walking from right to left, falling sideways, forward, and backwards, respectively.
* Scenario 4: The person is standing near the window and then immediately falls while turning back.
* Scenario 5: The person is walking from the left and then immediately falls down.
* Scenario 6: The person walks from the right, and sits on a chair. While getting up from the chair, she immediately falls down.
* Scenario 7: The person walks from the left, stands near a table and walks toward a chair near the window. After that, he immediately falls on the bedsheet.