## 2 Methods and Techniques for Enhancing Visitor Emotional Engagement
For the Support Vector Machine (SVM), a radial basis function (RBF) kernel was applied, as it effectively handles non-linear relationships in the data. To optimize the SVM's performance, we utilized a grid search for hyperparameter tuning, systematically exploring different combinations of parameters, such as the penalty parameter \(C\) and the kernel coefficient \(\gamma\). This approach enabled us to identify the optimal settings that yielded the best classification accuracy on the validation set. Physiological data, including heart rate and electrodermal activity, were gathered using wearable sensors and processed using signal-filtering techniques to eliminate noise. These methods provided a comprehensive view of emotional states by fusing data from multiple sources. To ensure the reliability of the emotional classification, the models were validated using cross-validation with ground truth data derived from self-reported emotional responses and physiological readings. Each modality comes with its set of advantages and limitations, making it essential to consider factors that contribute to the effectiveness of a particular modality, such as the following:  
1. **Validity of the Signal:** How naturally the modality aligns with the identification of an affective state in a museum context. 2. **Reliability in Real Environments:** The modality's capability to consistently and accurately capture emotional responses in the dynamic setting of a museum. 3. **Temporal Resolution:** The ability of the modality to provide timely data that align with the specific needs of the museum experience. 4. **Costs and User Intrusiveness:** The financial implications and how invasive or obtrusive the modality might be for the visitor. Here, we describe the main methods and techniques for affectivity detection in museums. **Facial Expressions:** This method capitalizes on the distinct facial expressions associated with each basic emotion [18]. Pioneered by [PERSON], this technique examines the universality of facial expressions and their recognition across various cultures. Many applications within museums have utilized facial expressions to decipher visitor emotions. **Body Language and Posture:** The utilization of body posture as an instrument for detecting emotional states has proven advantageous [19]. While facial expressions may sometimes reveal emotions, posture and body language can provide unique insights, especially those that have an unconscious nature [20]. **Vocal Emotion Recognition:** The voice within verbal communication carries a wealth of information, containing valuable features for determining emotional characteristics. Quantitative studies in vocal emotions have a longer history than studies of facial expressions. Vocal emotion recognition often involves analyzing \"prosodic\" information, which includes pitch, duration, and intensity [21; 22; 23]. **Physiology:** Museums increasingly employ machine learning techniques to identify patterns in physiological activity that correspond to the expression of different emotions. Most measures are noninvasive, based on electrical signals generated by the brain, heart, muscles, and skin. Advances in wearable physiological sensors have opened up new opportunities to infer visitor effects from physiology, overcoming some practical challenges [24]. **Multimodality:** Museums understand that affect recognition can encompass various modalities, including posture, gestures, voice, facial expressions, and diverse physiological signals. In real-time, dynamic museum settings, ensuring the reliability of multimodal data fusion (e.g., facial expressions, posture, voice) requires several strategies. Firstly, each modality is validated individually through cross-validation methods. For example, facial expression recognition uses Convolutional Neural Networks (CNNs) trained on large datasets, and the results are cross-validated with self-reported emotional states for accuracy.