# Feature Extraction Based on Sparse Coding Approach for Hand Grasp Type Classification
## 3 Sparse Coding Feature Extraction Based on Dictionary Learning Approach
## 6 Discussion and Limitations

In the discussion section, we analyze and interpret the results obtained from our experiments, focusing on various performance factors and how our proposed system addresses the challenges highlighted in the introductory part of the article. Firstly, we observed significant improvements in classification accuracy with the proposed sparse-coding-based feature extraction method compared to traditional techniques such as principal component analysis (PCA). As shown in Table 4, the sparse coding approach achieved an accuracy of 81.78%, outperforming PCA by a substantial margin. This improvement can be attributed to the ability of sparse coding to capture essential features while effectively reducing data dimensionality. By representing hand kinematic data as a sparse linear combination of basis functions, the sparse coding method can extract discriminative features crucial for accurate classification. Additionally, our proposed method offers versatility and applicability across a wide range of classification tasks. Furthermore, the number of features was significantly reduced using the proposed method (678,678 features) compared to raw data (1,620,299 features), PCA (855,000 features), and sparse coding based on Gaussian random dictionary (1,823,449). This reduction in feature dimensionality not only simplifies the computational complexity but also enhances the interpretability of the model. Moreover, the macro-average F1-scores and average AUC values for the proposed methods were substantially higher at 79.87 and 0.9535, respectively, compared to other methods, further highlighting the superior performance of our proposed approach. While the sparse coding approach using a Gaussian random dictionary (shown in Table 4) achieved a respectable accuracy of 77.27%, it fell short of the superior performance achieved by our proposed dictionary learning method (81.78% accuracy). This highlights the importance of learning an dictionary that has been optimized to the specific data for achieving the best results. In terms of addressing the issues highlighted, the proposed technique effectively tackles the challenge of feature extraction for hand kinematic analysis. By employing sparse coding based on dictionary learning techniques, we can extract meaningful features from

Figure 8: Comparison of AUC values for each class between raw data, PCA, sparse coding based on Gaussian random dictionary, and dictionary learning approach. high-dimensional kinematic data, reducing the complexity of the dataset while preserving essential information. This approach addresses the need for efficient data representation and dimensionality reduction in hand kinematic analysis, facilitating more accurate classification of grasping types. The experimental results validate the effectiveness of the sparse-coding-based feature extraction method based on dictionary learning for hand kinematic analysis and classification. By achieving higher accuracy compared to traditional techniques, these results indicate that our method not only enhances classification accuracy but also effectively reduces feature dimensionality, making it a valuable tool for practical applications in robotics, medicine, and rehabilitation. Future work could explore the application of our method to other types of hand kinematic data and investigate its performance in real-time systems. In this research, the proposed feature extraction technique was implemented specifically on the UNIPI dataset for classification purposes. Consequently, they may not be directly applicable to other publicly available datasets related to hand kinematics. Furthermore, while the proposed technique proves effective for hand kinematic analysis, it may not be suitable for analyzing hand synergies due to its design limitations. Future research could explore further optimizations and extensions of the proposed method as well as its application in other domains such as natural language processing.