# Emotion Recognition in Usability Testing: A Framework for Improving Web Application UI Design
## 4 Discussion  
The use of deep learning algorithms and data analysis makes the evaluation more objective, as it is based on mathematical models and actual data rather than the subjective opinion of the evaluators. This shift to data-driven assessments enhances the credibility and reliability of UX research results. Testing takes place in a real-time environment that reflects the actual user experience. This allows for a more accurate assessment of how users interact with the site and to identify real usability weaknesses. This real-time feedback can inform rapid design iterations and continuous improvement efforts. Incorporating emotion recognition into UX research emphasizes a user-centered design approach. By understanding users' emotions, developers can create more empathetic and intuitive web application interfaces that are tailored to users' needs and preferences. One could think about automating the system on a larger scale. Automated notifications when large numbers of negative emotions are captured would allow for quick response and resolution of issues, saving human resources and time. Automated testing processes could easily scale to large data sets and large numbers of users, enabling broader and deeper testing. These benefits show that usability testing with such a framework could be more efficient and more accurate in reflecting user needs and behavior than traditional web application testing methods. Figure 16: Use case #2: user engagement on different pages data from Google Analytics. The red frames highlight the pages with problematic UI in a use case #1 and #2. The emotion recognition technology could include more advanced algorithms or use additional sensors to provide more accurate data about users' emotions. Better emotion recognition results could also be achieved by ensuring that the lighting and camera quality are adequate to obtain clearer and better-quality images. It is also very important to balance the angle and composition of the shot to ensure that the angle of the cameras best matches the area of the face. To ensure that the emotion recognition system is universal and includes people of all nationalities and skin colors, the algorithm should be improved or more training data should be added to better recognize the emotions of people of different nationalities and skin colors. To obtain more information from users--and in line with usability testing methods--using additional features of Hotjar such as surveys and interviews would allow us to obtain valuable feedback from users and better understand their needs and behavioral patterns. As the number of users grows, more time should be spent delving further into Google Analytics and taking advantage of this powerful tool, including a better understanding of on-page analytics, user flow tracking, and conversion analysis, which can provide valuable insights into user behavior and website usage. The integration with tools like Hotjar and Google Analytics enhances the framework's practicality by leveraging existing UX research methodologies. This interoperability streamlines data collection and analysis, making the framework accessible to UX researchers and developers. Currently, the framework focuses on analyzing facial expressions from images captured by the camera. To extend its capabilities, speech analysis can be integrated by capturing audio at the same time as images. This would involve modifying the client side to capture both image frames and audio data, sending them to the Lambda function for processing. The Lambda function then parses and decodes the audio data and applies a separate audio emotion recognition algorithm trained on labeled data to identify emotional states. Techniques such as CNNs or RNNs that use audio features such as pitch and intensity can be used for this purpose. As datasets and input complexity increase, optimizing image and audio processing becomes critical for scalability.