# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 3 Proposed Hybrid Graph Embedding and Adaptive Convolution Networks
### Feature Extraction  
HGE-ACN sign language phrase recognition relies on the feature extraction and keypoint detection process Figure 2a below. It involves collecting and analyzing wearable glove motion and location data. The wearable glove has two main sensor types: six-axis IMU (Inertial Measurement Unit) sensors for three-dimensional motion tracking and finger curvature sensors for finger bending angles and hand shape configurations. The HGE-ACN technique relies on feature extraction through three parallel channels: Inertial Features, Motion Features, and Temporal Sequences. Motion Features measure velocity and acceleration patterns, identify movement segments, and assess dynamic signing elements, whereas Inertial Features extract acceleration and gyroscopic information. Temporal Sequences explore time-based patterns in signing, recognizing movements and places and capturing gesture durations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameter** & **Notation** & **Value(s)** & **Description** \\ \hline Graph Construction Threshold & \(\tau\) & 0.5–0.7 & \begin{tabular}{c} Defines connectivity in the spatial–temporal graph. \\ Determines feature embedding size in the latent space. \\ Controls receptive field in feature extraction. \\ \end{tabular} \\ Graph Embedding & d & 64, 128 & \begin{tabular}{c} Determines feature embedding size in the latent space. \\ Controls receptive field in feature extraction. \\ \end{tabular} \\ Dimensionality & k & \(3\times 3,5\times 5\) & \begin{tabular}{c} Controls receptive field in feature extraction. \\ \end{tabular} \\ Adaptive Convolution & k & \(3\times 3,5\times 5\) & \begin{tabular}{c} Controls receptive field in feature extraction. \\ \end{tabular} \\ Kernel Size & k & \(3\times 3,5\times 5\) & \begin{tabular}{c} Controls receptive field in feature extraction. \\ \end{tabular} \\ Normalization Factor in Edge & \(\sigma\) & 0.1–1.0 & \begin{tabular}{c} Regulates sensitivity of graph edge weights. \\ \end{tabular} \\ Weight Calculation & \(\alpha\) & 0.1–0.9 & \begin{tabular}{c} Adjusts attention-based feature importance. \\ \end{tabular} \\ Attention Weight Factor & P & 0.3–0.5 & \begin{tabular}{c} Previous overfitting in graph and \\ convolution layers. \\ \end{tabular} \\ Dropout Rate & \(\eta\) & 0.001–0.005 & \begin{tabular}{c} Determines optimization step size \\ for convergence. \\ \end{tabular} \\ Learning Rate & \(\eta\) & 0.001–0.005 & \begin{tabular}{c} Determines optimization step size \\ for convergence. \\ \end{tabular} \\ Batch Size & B & 32, 64 & \begin{tabular}{c} Number of samples processed per \\ training step. \\ \end{tabular} \\ Number of GCN Layers & L & 2, 3 & \begin{tabular}{c} Defines depth of graph \\ convolutional layers. \\ \end{tabular} \\ Number of Attention Heads & h & 4, 8 & \
\begin{tabular}{c} Controls multi-head \\ self-attention operations. \\ \end{tabular} \\ Weight Decay (L2 Regularization) & \(\lambda\) & \(1\times 10^{-4},1\times 10^{-5}\) &
\begin{tabular}{c} Reduces overfitting by penalizing \\ large weights. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameter configuration of HGE-ACN. In Figure 2a, feature extraction is a crucial step in processing sensor data for accurate recognition and classification. The process begins with sensor data acquisition, where real-time signals from multiple sensors are collected. Preprocessing enhances signal quality, followed by segmentation, where continuous data streams are divided into meaningful gesture segments. Features are extracted, including spatial, temporal, and motion-based metrics, such as velocity, acceleration, angular displacement, and curvature metrics. A spatial-temporal model is developed to capture intricate motion dependencies, improving  
Figure 2: (**a**) Feature extraction module. (**b**) WULALA data glove specifications. (**c**) A sign language gesture example. the representation of gestures.