## 4 Results
### Second Scenario: Video with a Passenger Running to the Bus  
In the second scenario, the video shows a person waiting for the bus, then walking towards the bus, and finally running to catch it (Figure 12). As the person moves through these stages, the model uses pose estimation to track the changes in their body posture and movements. When the person is simply waiting, their pose remains relatively stable, with minimal movement. As they begin to walk towards the bus, the model detects the characteristic walking motion, and when the person switches to running to catch the bus, the model recognizes the faster, more intense movement associated with running. By processing this sequence, the ML-RF model can accurately classify the activities as \"waiting\", \"walking\", and \"running\", which correspond to the different phases shown in the video. These transitions are important because they highlight the person's urgency to catch the bus, a common occurrence at bus stops, which can inform better planning for things like bus schedules or stop layouts. Figure 12 shows the pose estimation for the person present in the video. It can be observed that AlphaPose is able to accurately estimate the skeleton and pose of the person in most frames of the video, tracking the key points of their body and movements with precision. This allows the system to generate a reliable representation of the person's posture and movement at each moment in time. Using this pose estimation data along with the feature vector, the ML-RF model predicts the activities being performed by the person. Figure 13 presents the activities recognized by the ML model throughout the video. It shows that the model identified  
Figure 12: Passenger running to get on the bus in Valparaso, Chile. 7 activities in total: falling to the knees, falling backward, using an object, sitting down on a chair, walking, walking towards the camera, and standing. This variety of recognized activities suggests that the model can detect a wide range of human movements. Some activities, such as falling or using an object, might be misclassifications or rare occurrences, while others, like walking or standing, are more likely to be consistent throughout the video. The ability to accurately predict and classify such activities provides valuable insights into the person's behavior at the bus stop, which could be useful for improving the user experience and enhancing the design of the public transportation system. For example, recognizing activities like \"walking towards the camera\" or \"standing\" could help identify patterns of passenger movement that lead to delays or other inefficiencies at bus stops. Figure 14 shows the activities performed by the person in the video for each frame. It can be observed that at several moments in the video, the model recognizes activities that do not occur, such as different forms of falling (e.g., falling to the knees or falling backward). These misclassifications can arise due to subtle or rapid movements that the model might mistakenly interpret as falls, even though they are simply the person adjusting their position or shifting their posture. Such misclassifications often happen when the pose estimation is not as clear or when certain body movements resemble the patterns associated with falling. To gain a clearer understanding of which activities are being correctly and incorrectly recognized, the graph in Figure 15 is generated. This graph highlights classification accuracy over time, helping to distinguish between valid predictions and erroneous ones. By examining this graph, we can see where the model makes mistakes, such as incorrectly predicting a fall when the person is merely walking or changing positions. Figure 13: Frequency of activities recognized by the ML-RF model throughout the video. This analysis helps refine the model by focusing on the periods where errors occur and adjusting the system to improve its accuracy in identifying specific activities.