# Predicting Team Well-Being through Face Video Analysis with AI
## 4 Results
### Data Preparation  
The first phase is the Data preparation. The panoramic video files are preprocessed to extract pertinent information usable by the machine learning models. First, the proposed FAS presented in Section 3 is used to generate the dataset of features related to each individual in each video. It extracts multiple initial features directly from the video stream in a time series structure as summarized in Table 1.  
The human field of view (FOV) angle for 3D gaze pattern estimation is set to 60\({}^{\circ}\). A window size of 30 s is chosen for the rolling average on face emotion to reduce noise. Then, each record is linked to the associated PERMA labels \(\mathbf{Y}\). The PERMA data in the \(\mathbf{Y}\) dataset are preprocessed to handle missing values and outliers. Also, both the \(\chi\) and the \(\mathbf{Y}\) dataset are normalized to be used in the machine learning models. Thus, in the _Data preprocessing_ step, all records linked to a missing value or to a constant value throughout all the pillars of the PERMA survey are removed. PERMA variables are normalized using a min-max normalization while the dataset features are normalized using a standard or robust scaling depending on their distribution [41]. The PERMA variables contained in the \(Y\) dataset are continuous variables. Regression is therefore the most straightforward data analysis model. However, it may also be useful to classify each variable into binary categories (high- or low-level), as this aligns with the overall goal of the research. Classification metrics offer more intelligible performance scores than regression metrics [42]. Thus, a new dataset called \(Y_{bin}\) is generated by discretizing the \(Y\) dataset. The discretization is carried out by applying a median threshold to each dimension of \(Y\) for binary classification. In order to reduce the complexity of the methodology and provide interpretable results, each targeted variable \(tar\) present in \(Y\) and \(Y_{bin}\) is analyzed independently in univariate problems. To further limit the complexity of the models and comply with [PERSON]'s razor principle, the features extracted in \(\chi\) are then selected in the _Feature selection_ step to generate the \(X\) dataset. The attribute selection method is preferred to the dimensionality reduction method for reasons of interpretability of the results [43]. To perform feature selection only within the training set to prevent data leakage, the \(\mathbf{X}\), the \(\mathbf{Y}\), and the \(Y_{bin}\) datasets are divided into a training set (\(X_{train}\), \(y_{train}\), and \(y_{bin_{train}}\)) and a test dataset (\(X_{test}\), \(y_{test}\), and \(y_{bin_{test}}\)) representing 80% and 20% of the total dataset, respectively. Then, a voting strategy among filters presented in Table 2 is defined for feature selection. Those filters are chosen since they are relatively computationally efficient and model-agnostic. Sets of features are evaluated for each target variable \(tar\) by the voting system using Equation (2). \[S(\Phi)=\sum_{id=1}^{7}w_{id}\cdot S_{id}(\Phi) \tag{2}\]  
where \(\Phi\) represents the set of features considered, \(id\) the filter ID, \(S_{id}(\Phi)\) the ensemble scores from the filter \(id\) for all features in \(\Phi\), and finally, \(\omega_{id}\) represents the weight given to the filter \(id\) based on the importance of the filter to the issue at hand [43]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Filter ID** & **Name** & **Reference** \\ \hline
1 & Univariate Linear Regression/ANOVA F-value & [44] \\
2 & Mutual information & [45] \\
3 & Variance thresholding & [44] \\
4 & Percentile of the highest scores & [46] \\
5 & False Positive Rate & [47] \\
6 & False Discovery Rate & [47] \\
7 & Family wise error rate & [47] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of filters used.