## 3 Analysis Model  
The current research constructed models using feedforward deep learning algorithms and convolutional neural networks. As a result, four different models were created, as presented in Figure 3. The participants that were underweight or that had healthy weights, BMI 1 and BMI 2, were merged into a group and labeled \"0\" in Dataset 1 of Figure 3, as supervised learning. The overweight and obese participants, categorized as BMI 3 or BMI 4, were merged into a group and labeled \"1\" for the purposes of training and testing the four models presented in Figure 3.  
For example, BMI 3 (overweight), in Dataset 1, is in the obese group. BMI 1, BMI 2, and BMI 3 in Dataset 2 of Figure 3 are labeled \"0\". Thus, BMI 3 (overweight) in Dataset 2 is in the normal group. BMI 4 in Dataset 2 is labeled \"1\". To train and test the four different models, the students were divided into two groups. One group, composed of the rotation vector data of 119 students, based on the X, Y, and Z data points, was utilized for training the proposed models, Model 1-1 to Model 2-2. For the other groups, the rest of the student data was used to test the proposed models. We analyzed the subjects' nine rotation vectors' components, along the X, Y, and Z axes. We removed the first and last five seconds of the walking data to prevent the overfitting of the model. Within the average time spent for the round trip per respondent, approximately 3000 (times) \(\times\) 9 (nine vectors) data points were collected. Dataset 1 and Dataset 2 in Figure 3 were fed to train the feedforward neural network for creating Model 1-1 and Model 1-2. Again, both datasets were fed to train the convolutional network for creating Model 2-1 and Model 2-2. The data points in Dataset 1 and Dataset 2 in Model 1-1 and Model 1-2 were shuffled by the sample function [37] in R and were normalized by min-max normalization before training the feedforward neural network. Moreover, the input shape of Model 1-1 and Model 1-2 is 9: nine vectors of rotation. The data points in Dataset 1 and Dataset 2 in Model 2-1 and Model 2-2 were normalized by min-max normalization. Then, 500 data points of each student were extracted to be used as one input data point in the convolutional neural network. The resulting input shape of Model 2-1 and Model 2-2 is 9 (nine vectors of rotation) \(\times\) 500 \(\times\) 1 (output labels). Using Keras [38] and Tensorflow [39] with R, we build Algorithm 1 employing the feedforward neural network, and Algorithm 2 using the convolutional neural network. The feedforward neural network in Algorithm 1 uses one input layer, five hidden layers, and one output layer. The input layer is initialized by Glorot normal initialization [40]. The input shape of the input layer is nine rotation vectors. The activation function of the input and hidden layers uses the Relu function [41], and each layer has 512 neurons, and 50% of the neurons are randomly dropped to prevent the overfitting of the input data. The output layer has two neurons for distinguishing the group of students with obesity from the normal group. The activation function of the output layer uses the Softmax [42] function. Figure 3: Training the feedforward and the convolutional neural network models.