# Predicting Team Well-Being through Face Video Analysis with AI
## 5 Discussion  
To recall, the aim of the proposed study was to understand the non-verbal communication process in teamwork using video data and identify significant predictors of individual well-being in teamwork. The experiment conducted and the results obtained serve as a basis for discussion of the proposed research questions. **RQ1: Which features of videos taken in a team setting will be predictive of individual and team well-being measured with PERMA surveys? **  
Through combining video analysis with measuring well-being with PERMA, we identified relevant features predicting individual well-being: If individuals exchange more gazes with others, experience more surprises, behave more dominant (i.e., speak more), and are more emotionally stable, they report higher satisfaction measured through PERMA. This means that simply experiencing constant happiness is not the best way to achieve a positive team experience; rather, individuals should engage in an active social exchange with others and pursue surprising avenues in their work. A framework combining state-of-the-art tools has been proposed in Section 3 extracting from panoramic video data non-verbal cues, such as facial emotions, gaze patterns, and head motions as input for individual well-being analysis. An experiment presented in Section 4 applies the proposed framework and links the extracted attributes to the results of PERMA+4 surveys evaluating the various pillars of well-being defined in positive psychology. This way, a dataset of 125 features has been generated to predict the different pillars of the PERMA analysis. Machine learning models were then trained for the regression and binary classification tasks to predict individual well-being scores, as defined by the PERMA framework. When applied to a case study of collaboration within 20 co-located work teams, regression models outperform the baselines in four of the five PERMA dimensions, with a notable 1.5% improvement in MAE. Bayesian ridge regression was identified as particularly effective. In comparison, binary classification emerged as a more reliable approach, with models yielding a balanced accuracy improvement of 5.1%, also outperforming the baseline in four out of five PERMA dimensions. Ensemble models, specifically CatBoost, showed superior performance in this setting. Notably, the _Meaning_ dimension of PERMA proved challenging in both prediction and classification settings, indicating difficulty in discerning a participant's sense of meaning purely from video cues. **RQ2: How can the relevance of attributes for predicting individual well-being in a collaborative work context be measured? **  
In this work, we have developed an approach measuring self-reported well-being from facial expressions and upper body posture. Using SHAP values to measure the size of the attributes for predicting PERMA we can identify the most predictive attributes. Applying this approach, we found for instance that active exchanges of mutual glames and engaging in an active dialog increase the well-being of team members. SHAP values are used to interpret the impact of features on prediction and classification, independently of the machine learning model used. They also rank features according to their importance for the model under study [51]. Derived from cooperative game theory, SHAP values identify the importance of features for each data point, since they are decomposed into the sum of feature contributions. This provides a more transparent description of the model's behavior and therefore greater interpretability of the models[51]. Furthermore, this approach facilitates the identification of the most appropriate features for PERMA prediction by allowing the comparison of the influence of features across multiple models [51]. **RQ3: How can theories and hypotheses relevant to positive psychology be derived from AI-driven team video analysis?