# Towards Detection of Interest Using Physiological Sensors
## 3 Physiological Patterns and Features of Interest
### Patterns Obtained Using Other Sensors
These affective states were high interest, low interest, and taking a break. Medium interest was eliminated as an affective state because the model always confused this state with the high- or low-interest states, which resulted in poor performance. The final model had an average accuracy of 82.25% for classifying high interest, low interest, and taking a break. The study provided evidence that the observed patterns in the dynamics of the student's postures contained significant information related to the affective states of high interest, low interest, or taking a break. However, higher sample sizes are required in physiological studies to enhance model robustness and to obtain more validated results. The authors of this study also did not explain how they measured or quantified high vs. low interest or the theoretical or experimental basis for this measurement. [PERSON] et al. (2018) studied the applicability of physiological sensors in detecting student emotions during intelligent tutoring classes. As shown in Table 3, the authors performed two experiments: one with school students and another with undergraduate students. They used self-reports with several physiological sensors, including a camera for facial expression recognition, pressure-sensitive seat cushions and back pads with audiometers, pressure mouse devices, and wireless conductance bracelets. These sensors were used to detect four emotional conditions: interested, confident, excited, and frustrated. The aim of the research was to find an alternative to the use of classical contextual variables such as time spent to solve a problem, number of hints given by the tutor, and number of attempts. The authors found a significant correlation between pre-test scores and interest. In particular, students with better mathematical knowledge reported higher interest than those with less extensive mathematical knowledge. The authors showed that the feeling reported during each self-report question was related to what the subjects faced just before the question was presented. Therefore, when a subject self-reported, \"I feel frustrated\", it was likely because they had one or more incomplete attempts in solving the previous problem. Similarly, when a subject reported, \"I feel confident\", it was because he/she had just solved a problem. This was verified using stepwise regression, which indicated that interest (variance of 14%) could be predicted based on incorrect attempts and the gender of the learning companion. One limitation of this proposal was the unavailability of all the sensors used at the same time for all students due to several practical problems. The contribution from the camera sensor alone accounted for 52% of the variance in detecting confidence. The camera had similar contributions to the detection of other emotions. Using these sensors can help the tutor predict more than 60% of the variance in the student's emotional state. The R values for the fit models used for the four emotions when using all the sensors and the tutor were 0.82, 0.72, and 0.70 for the confident, frustrated, and excited states, respectively. However, no fit model was found for the interested state. As mentioned earlier, the sensor data were only analyzed for 50% of the students due to difficulties in assessing all of the subjects using all the sensors simultaneously. The study by [PERSON] et al. [86] represents one of several studies that uses facial expressions to detect emotions and interest e.g., [89; 90] (for a review, see [91]). However, only the [PERSON] et al. study is discussed here because it used information from six emotions (surprise, sadness, fear, anger, happiness, and disgust) to measure the level of interest of subjects while they watch different movie clips. This study thus proposes a new indirect way of measuring the level of interest of an individual while he/she interacts with a machine. To measure the level of interest, the authors used a three-dimensional affect space (discussed in [92]).