## 4 Results and Discussion  
This paper employs Ubuntu18.04 as its operating system and accelerates training using CUDA10.0 and cuDNN7.4. The NVIDIA RTX 3080 is used as the hardware platform. PyTorch.10 serves as the network development framework, with Python as the programming language. The model utilizes an Intel(r)Core(tm)i5-9400F [EMAIL_ADDRESS] GHz as the CPU. The model trains for 80 rounds with the optimization algorithm SGD, utilizing an initial learning rate of 2 \(\times\) 10\({}^{-4}\) and a batch size of 128. Additionally, given the small data dimension, the model employs a dropout strategy with a parameter of 0.3 to prevent overfitting. In this paper, the confusion matrix is used as the evaluation standard to evaluate the model, and the expression of the confusion matrix is shown in the formula. \[ \begin{array}{c}1\quad\ldots\quad N\\ 1\quad\vdots\\ N\quad\begin{bmatrix}n_{11}^{T}&\cdots&n_{1N}^{T}\\ \vdots&\ddots&\vdots\\ n_{N1}^{T}&\cdots&n_{NN}^{T}\\ P_{1}&\cdots&P_{N}\end{bmatrix}&\begin{array}{c}C_{1}\\ \vdots\\ C_{N}\end{array}\end{array}  \tag{9}\]  
Each column of the confusion matrix represents the predicted category, each row represents the true attribution category of the data, and the data summary of each row indicates the number of data instances of this category. Among them, nA _ij_ represents the number of category \(i\) predicted as category \(j\); when \(i\) = \(j\), it means that the prediction is accurate, A = T, and when \(i\) = \(j\), it means that the prediction is wrong, A = F. Pi represents the total number of predicted categories as class \(i\) and C represents the total number of true classes \(i\). According to the confusion matrix, the precision rate (Precision), recall rate (Recall), and F1 value can be calculated separately to further evaluate the model. The F1 value is a balance point between Precision and Recall, which can measure the distribution of the test population; its calculation formula is as follows:  
\[ \begin{array}{c}Precision=\frac{TP}{TP+FP}\times 100\%\\ \\ Recall=\frac{TP}{TP+FN}\times 100\%\end{array}  \tag{10}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer Name** & **Kernel Size** & **Kernel Number** & **Padding** & **Stride** \\ \hline Conv1 & 3 \(\times\) 3 & 16 & 0 & 1 \\ Conv2 & 3 \(\times\) 3 & 32 & 0 & 1 \\ Conv3 & 3 \(\times\) 3 & 64 & 0 & 1 \\ FC & - & 26 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter configuration of the trainable layer. \[F1=\frac{2\times\textit{Precision}\times\textit{Recall}}{\textit{Precision}+\textit{ Recall}}\times 100\% \tag{12}\]  
Among them, _TP_ is True Positive, _FP_ is False Positive, _FN_ is False Negative, and _TN_ is True Negative. The trained model is classified using the test set. The confusion matrix is shown in Figures 10 and 11, and the Precision, Recall, and F1 scores are shown in Table 3.  
The table above clearly indicates that both models achieve evaluation indicators exceeding 93%. However, the model based on a one-dimensional convolutional neural network performs exceptionally well, with indicators almost reaching 97%. This gives it an advantage over the LSTM-based model. The reason for this discrepancy could be that the interdependence of sample data in this paper, in the time dimension, is not strong enough to extract deep feature information based on sequence classification. Therefore, the paper proposes the use of the CNN-Ges-Cla model, which is based on CNN-1D, to classify the collected gesture signals. The use of magnetic detection technology based on the geomagnetic field has been widely adopted in the field of indoor positioning. However, this paper proposes an innovative application of this technology to the field of gesture recognition. The proposed method involves using six geomagnetic field sensors installed on the human hand to obtain gesture data. We trained a 1D convolutional neural network using magnetic field data from predefined gestures, and the accuracy of gesture recognition reached close to 97% under experimental conditions.