## 4 Research Process
### The User Satisfaction Model Based on Objective Data  
Upon completion of the experiment, we found that the improper sitting position of two participants during the experiment resulted in the camera not capturing their frontal expressions; therefore, the data for these two subjects were considered invalid, leaving 42 valid datasets. Group A comprised subjects 1-21, and Group B included subjects 22-42. The objective data from the 21 users in Group A were coded as matrix \(I_{A}\), and the objective data from the 21 users in Group B were coded as matrix \(I_{B}\). Matrix \(I_{A}\) served as the input for training the BP neural network model, as outlined in Equation (6), while Matrix \(I_{B}\) was used for model validation, as detailed in Equation (7). The objective data for the m-th subject were denoted as Im, as shown in Equation (8). The variables \(i_{1}\) to \(i_{7}\) represented the intensity of the seven emotions of joy, sadness, anger, surprise, fear, disgust, and neutrality, respectively. The variable \(i_{8}\) represented the task completion time, \(i_{9}\) denoted the task completion status (1 for completed, 0 for uncompleted), and \(i_{10}\) reflected the disorientation degree. The user satisfaction ratings, serving as the output values, were encoded as matrix \(Y_{A}\) for Group A subjects, as indicated in Equation (9), and as matrix \(Y_{B}\) for Group B subjects,  
Figure 4: Task test pages for Goodnotes and Notability.  
as specified in Equation (10), with Ym representing the satisfaction rating of the m-th subject.  
\[I_{A}=[I_{1},I_{2},I_{3},I_{4},I_{5},I_{6},I_{7},I_{8},I_{9},I_{10},I_{11},I_{12}, I_{13},I_{14},I_{15},I_{16},I_{17},I_{18},I_{19},I_{20},I_{21}]^{T} \tag{6}\]  
\[I_{B}=[I_{22},I_{23},I_{24},I_{25},I_{26},I_{27},I_{28},I_{29},I_{30},I_{31},I_{32 },I_{33},I_{34},I_{35},I_{36},I_{37},I_{38},I_{39},I_{40},I_{41},I_{42}]^{T} \tag{7}\]  
\[I_{m}=[I_{1},I_{2},I_{3},i_{4},i_{5},i_{6},i_{7},i_{8},i_{9},i_{10}] \tag{8}\]  
\[Y_{A}=[Y_{1},Y_{2},Y_{3},Y_{4},Y_{5},Y_{6},Y_{7},Y_{8},Y_{9},Y_{10},Y_{11},Y_{ 12},Y_{13},Y_{14},Y_{15},I_{16},Y_{17},Y_{18},Y_{19},Y_{20},Y_{21}]^{T} \tag{9}\]  
\[Y_{B}=[Y_{22},Y_{23},Y_{24},Y_{25},Y_{26},Y_{27},Y_{28},Y_{29},Y_{30},Y_{31},Y _{32},Y_{33},Y_{34},Y_{35},Y_{36},Y_{37},Y_{38},Y_{39},Y_{40},Y_{41},Y_{42}]^{T} \tag{10}\]  
The user satisfaction model was established using the PSO-BP neural network. In this study, the input parameters were represented by matrix \(I_{A}\). Following the BP neural network algorithm, there are 10 input nodes, 1 output node, and 3 hidden layer nodes. The maximum number of iterations was set to M = 50. And based on the characteristics of the data in this study, MAPE was selected as the loss function. According to the PSO algorithm, the initial position and velocity of particles were randomly generated within the allowable range, with the population size selected as three times the dimension (N = 30). Additionally, the parameters \(w_{\text{max}}\) = 1, \(w_{\text{min}}\) = 0.02, \(c_{\text{1 min}}\) = 0.05, \(c_{\text{1 max}}\) = 1.2, \(c_{\text{2 min}}\) = 0.05, and \(c_{\text{2 max}}\) = 1.2 were set. The optimized weight threshold of the PSO algorithm was assigned to the BP neural network, enabling the training of the BP neural network to achieve a prediction model with high accuracy.