# Feature Extraction Based on Sparse Coding Approach for Hand Grasp Type Classification
## 3 Sparse Coding Feature Extraction Based on Dictionary Learning Approach
Additionally, principal component analysis (PCA) was employed to extract features from the pre-processed data, serving as a benchmark for comparing the efficiency of the proposed technique with the traditional technique for classifying grasping types. The selection of PCA as a benchmark was influenced by this research and was based on its linearity and well-known effectiveness for hand-based feature extraction tasks. Specifically, we chose two principal components for experimentation in benchmarking. This choice aimed to capture essential information while minimizing the

Figure 5: Kinematic model of the human hand. number of components and ensuring a meaningful representation of the entire dataset's variance. These criteria provide a foundation for a meaningful and informative comparison with the proposed sparse coding approach. The final step involved classification based on neural network classification, where the goal of classification was to identify grasping types based on hand kinematic data. The sparse coefficients from the training set were used to train the NN classification model. The sparse coefficient from the test set was utilized to evaluate the performance of the NN classification model. The classification results are visualized by using a confusion matrix. The proposed feature extraction process, along with PCA as the feature extraction technique, incorporated sparse coding based on dictionary learning. The dictionary was constructed using a dictionary learning function [32]. Sparse coefficients were computed using a sparse decomposition toolbox [33], which implemented the LARS algorithm to enhance the effectiveness of solving the lasso problem and accelerate the process. To evaluate the performance of the proposed technique and compare it with PCA, we utilized a neural network classification toolbox. The experimental results obtained from the neural network classification are presented in Table 4, which showcases a comparison of the proposed method with other approaches. These approaches include using raw data (preprocessed without feature extraction), principal component analysis (PCA), sparse coding based on a Gaussian random dictionary, and the proposed dictionary learning technique. The comparison of accuracy was evaluated using confusion matrices for four cases: without feature extraction, PCA, sparse coding based on a Gaussian random dictionary, and the proposed method, as shown in Figure 6a,b,c,d, respectively. Table 4 shows significant differences in classification accuracy. The PCA method achieved an accuracy of 31.43%, which is markedly lower than that of the proposed method, which demonstrated an accuracy of 81.78%. Additionally, it is remarkable that the accuracy of the PCA method is significantly lower than that of the raw data, which yielded an accuracy of 68.38%. Sparse coding based on the Gaussian random dictionary achieved an accuracy of 77.27%, demonstrating better performance than PCA but still lower performance than the proposed dictionary learning technique. Figure 7a-d shows the ROC curves of each feature extraction techniques, it is clearly to see that the proposed method provides higher recall rate compare with other techniques. Moreover, Table 4 includes the number of features used with raw data, PCA, the Gaussian random dictionary, and the proposed dictionary learning technique. The proposed technique achieved the highest accuracy with the fewest number of features for grasp type classification using hand kinematic data. Figure 6: Confusion matrices for NN classification using several feature extraction techniques: (**a**) raw data, (**b**) PCA, (**c**) sparse coding based on Gaussian random dictionary, and (**d**) sparse coding based on dictionary learning. Figure 7: ROC curve of feature extraction techniques: (**a**) raw data, (**b**) PCA, (**c**) sparse coding based on Gaussian random dictionary, and (**d**) sparse coding based on dictionary learning.