# Towards Detection of Interest Using Physiological Sensors
## 3 Physiological Patterns and Features of Interest
### Patterns Obtained Using Other Sensors  
Several other types of sensors are used to detect interest in subjects while they view stimuli. These sensors may include pressure sensors that are placed on the subject's seat, conductance bracelets, and pressure mouse devices. The authors of the following studies, which are listed in Table 3, evaluated the interest of subjects while they played an educational game, watched movies and used a tutoring system. More details regarding these studies are described below. We discuss the use of multimodality systems in this section. The studies discussed previously in this review used telemetry biopotential measures such as EEG and ECG, which measure the electrical potential between two electrodes, or eye-tracking. Here, pressure mouse devices, pressure seats, cameras, and conductance bracelets are used to measure variables related to interest. There is thus a diversity of physiological sensors used to detect interest. [PERSON] et al. [85] proposed the use of a multi-sensor affect recognition system to classify interest and disinterest in children while solving an educational puzzle on a computer. The approach was based on using a mixture of Gaussian processes to analyze facial features, current posture, and level of activity, as determined using a camera and a pressure seat, respectively. Information regarding the level of difficulty and the state of the game were obtained from the computer. The postures were labeled by teachers who viewed the camera and the chair details before the experiment. Posture was the most discriminating feature and had an accuracy of 81.97%. The accuracy for discrimination between interesting and uninteresting stimuli increased to 86.55% after considering all of the other features. Only 50 samples out of the 136 had features from all the modalities. The remaining samples lacked details from the face channel. The number of samples classified as \"interested\" was 65 and the number of samples considered \"uninterested\" was 71. Only 9 children were involved in this study. This highlights the need for studies with a larger sample size. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Study** & **Purpose** & **Participants** & **Emotion** & **Technique** & **Stimuli** & **Measurement** & **Features** & **Classifier** & **Remarks** \\ \hline \multicolumn{1}{c}{_Time_ microwave sensors_} & & & & & & & & & & \\  & & & & & & & & & & & \\  & & & & & & & & & & & \\  & & & & & & & & & & & \\  & & & & & & & & & & & \\  & & & & & & & & & & & \\  & & & & & & & & & & & \\  & & & & & & & & & & & \\  & & & & & & & & & & & \\  & & & & & & & & & & & \\  & & & & & & & & & & & \\  & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detecting interest using other physiological sensors. The study by [PERSON] et al. (2021) is the oldest of the studies discussed in this paper. This study is similar to the previous experiments outlined in Table 3 in terms of subject age and the sensors used, but differs in its purpose and classification algorithm. This study is also slightly different from the other studies in terms of features extraction. The purpose of this study was to measure the level of interest in learners while they played a constraint satisfaction game called Fripples Place over a period of 20 min. After acquiring the data using a camera and a pressure chair, three teachers separately labeled the data based on videos recorded by the cameras. These classifications had an average agreement of 78.57%, and this strategy was used because children are less likely to provide accurate statements about their feelings than adults are. After data pre-processing and noise removal, the data were modeled using a mixture of Gaussian processes. The Gaussian parameters were then fed into a 3-layer feed-forward neural network, which achieved an average static posture classification accuracy of 87.64%. Hidden Markov models (HMMs) were then used to model posture sequences for the affective states identified earlier by the teachers.