## 3 Methodology
### AI Selected Model  
This study employs the Random Forest (RF) algorithm described in [7] in combination with skeleton data estimated by AlphaPose to recognize human activities in real-world public transportation scenarios. The selected model not only was the best-performing classifier in our previous controlled experiments (F1-score > 96%) but also presented key advantages such as robustness to noisy data, low computational cost, and interpretability--critical factors in uncontrolled, dynamic environments like bus stops. The use of RF is strategic in this pilot study. The primary objective is to evaluate the feasibility and reliability of a human activity recognition (HAR) pipeline in realistic transit environments, rather than to propose a novel deep learning architecture. RF provides a robust and explainable baseline model that enables validation of the full methodology--from pose estimation to activity recognition--before exploring more complex models such as LSTM networks, graph convolutional networks (GCNs), or transformer-based architectures. Furthermore, compared to deep learning models, RF is computationally efficient, making it suitable for edge-computing scenarios or real-time applications in public transport systems. Unlike sequential models, the current approach does not rely on temporal dynamics over long sequences, but rather on short frame-level or window-level posture features. This justifies the use of a non-sequential, tree-based model for classification. It is important to emphasize that the novelty of our approach lies not in the machine learning algorithm itself but in its application: we are extending the use of a well-established AI pipeline (AlphaPose + RF) to a new and highly challenging context--real-world public bus stops--where multiple people interact dynamically and occlusions, noise, and posture variability are common. To our knowledge, this is among the first studies to validate a skeleton-based HAR model under real urban conditions. This study thus serves as a steppingstone: it demonstrates the limits and potential of classical approaches in noisy, multi-person environments, and sets the foundation for future work using more advanced deep learning models like our in-development BERT-based architecture for skeleton data. This work used the best machine learning model (Random Forest) described in [7] with the CAM (camera) modality through skeletons. Therefore, this human activity recognition model is selected to test if this methodology also works with more modern techniques. The model classifies the activities of passengers at the bus stop and when boarding and alighting the bus. The feature vector used to predict the activity was composed of sequences of skeletal poses estimated by AlphaPose. The selection of the Random Forest (RF) model from [7] was based on its ability to handle datasets with high dimensionality, its good performance (over 90%), and its robustness to noise in measurements. The Random Forest (RF) algorithm [26] is a supervised learning technique that generates multiple decision trees on a training dataset; the results obtained are combined to produce a single, more robust model compared to the results of each individual tree [27]. In [7], an RF model is trained on the UP-Fall dataset for activity recognition, with hyperparameters configured as shown in Table 1. \begin{table}
\begin{tabular}{l l} \hline \hline
**ML Model** & **Parameters** \\ \hline \multirow{3}{*}{Random Forest (RF)} & estimators = 10 \\  & min. samples splits = 2 \\  & min. samples leaf = 1 \\  & bootstrap = true \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters defined for the RF model in [7]. The model was trained and validated using the UP-Fall dataset [28] and tested with two datasets: UP-Fall and UR-Fall [29]. Everything was programmed in Python 3.10.