# Questionnaires or Inner Feelings: Who Measures the Engagement Better?
## 4 Discussion  
The work presented here aims to evaluate the level of engagement of a sample of users, who represent the target audience of the 3D simulator. To increase the involvement, a crucial point for the success of a new product or service, the contents must be interesting and useful for the final customers.  
Recent technological development and advances in computer vision have enabled an optimized approach to guarantee an efficient and effective product life cycle, affecting the early stages of design. This research area includes the analysis of the human face and the recognition of facial expressions and emotions for the automatic evaluation of spontaneous feedback.  
In this case study, facial analysis techniques were integrated with traditional questionnaires for two main purposes: first, starting from the videos acquired by the sensor, to develop a method capable of measuring involvement; second, to compare the results obtained from the two different methods and evaluate the advantages brought by the FER analysis.  
We filmed 24 people taking part in the simulator test with a 3D sensor when replying to the UES questionnaire. The two methods classified 14 participants out of 24 with the same level of involvement. For the remaining users, the UES and FER methods were not in agreement with each other, with on average more positive feedback of the questionnaires. Based on the users' answers, 8 were strongly activated, 15 were activated to an average extent and only 1 was poorly activated, while based on the images acquired by the sensor, they were 2, 19, and 3 respectively. Therefore, considering the median scores and the class classification of the images labeled for each user, user number 6 and user number 23 had the highest level of engagement.  
The UES questionnaire for users 3, 7, 8, 10, 11, and 12 assessed a high level of engagement (a median score equal to 6 or 7), but from the FER analysis it resulted that the largest number of frames acquired for these subjects fell into the intermediate activation class. A similar situation was found for users 2, 16, 19 and 22. They fell into the averagely activated class considering only the results of the questionnaire, while they fell into the deactivated class by the analysis of facial expressions. This could indicate that the actual level of involvement perceived by these subjects was lower than as stated. By comparing the results obtained with those of the previous work on the evaluation of involvement in a virtual learning environment [43], the use of neural networks has led to an improvement of the method. In fact, the correspondence between UES and facial expressions has increased from 50% to 58%. However, the main aim was not to achieve a perfect correlation between UES and neural networks, but to develop a new approach in which the two methods support each other. The future goal is to improve the assessment of engagement through the study of facial expressions.  
Questionnaires and FER systems can be integrated into an innovative method for evaluating user engagement, combining information and increasing knowledge for the design of the new product or service. If traditional methods are based on weighted answers, FER methods catch the spontaneous reactions in real-time.