# Towards Detection of Interest Using Physiological Sensors
## 3 Physiological Patterns and Features of Interest
### Eye-Tracking Patterns
This information was then stored in the learner's records for future reference. One advantage of this system was its ability to build and update different profiles for different learners based on both their current behavior and their stored profile. In other words, the system considered the individual differences of the learners and their different modes of behavior and learning at different times. Another advantage of the system was providing feedback to enhance the learning process. For instance, a highlighted text modified based on the subject's state is shown to an inattentive subject. The architecture of the model consisted of head pose estimation, eye gaze estimation, positions of hands during the video, a fraction of the inter-ocular distance between frames (by monitoring the variation in the distance between the user and the computer screen), and facial features (eye blinking, frowning, mouth opening, eyes wide open, or not moving at all). These features were fed into a neuro-fuzzy inference system, which then decides the status of the user as frustrated, struggling to read, distracted, tired/sleepy, not paying attention, attentive, or full of interest. In this experiment, the subjects had difficulty decoding written text (dyslexia). As a result, dyslexia experts annotated the video clips used as \"ground truth\". The actual features of the testing set were the region around the eyes, eyebrows, mouth, and/or features related to hand location and movement. Only two states were detected and reported. The accuracy was 100% for attentive individuals and 72% for inattentive individuals, with an average absolute error between the output and annotation of 0.117. The state of the user was noted as attentive if the output value was greater than 0.5 and inattentive otherwise. However, the algorithm was still incapable of detecting some features, such as \"frozen lips\". Therefore, some states were not accurately detected or not detected at all. The work by [PERSON] and [PERSON] [38] is interesting because it utilized a real-life application. The interest of subjects was detected based on eye-gaze patterns in an interactive system called iTourist, which is used for city trip planning. Two types of eye-gaze patterns associated with a person's interest in an object on the screen were identified. The first pattern comprised accumulated time and high intensity of a participant's gaze toward an object on the screen. The second pattern comprised the gaze of the participant on the surrounding objects related to the object of interest. The authors also found that frequent eye-gaze switching between two objects indicated an interest in the relationship between the two objects, e.g., participants were found to switch eye gaze between a hotel and a museum when they were interested in the museum and wished to book a nearby hotel. The system, which was implemented fully in C++, provided information to the users regarding an interesting place based solely on eye-gaze information. However, it had some limitations. For example, it falsely provided information that the participants were not interested and provided this information for a while before stopping. In addition, the system often missed providing information regarding places of interest. Nevertheless, the overall reaction of participants was quite positive. The studies discussed above showed the potential of using an eye-tracking system to detect subject's interest. The availability of such equipment and the high obtained accuracy especially in educational and tourism perspectives makes it promising and opens the door for more creative ideas. The relatively few numbers of studies compared to EEG modality suggest the need of further research in this area. However, the studies also showed that eye-tracking needs to be combined with other modalities to improve its performance. This is because the eye movement is an indicator of several conditions, and using it separately could result in an inaccurate result.