# Feature Extraction Based on Sparse Coding Approach for Hand Grasp Type Classification
## 1 Introduction
Subspace-learning-based approaches [16; 17; 18; 19; 20; 21; 22] are another category. These methods, like principal component analysis (PCA) [16; 17; 18; 19; 21; 22] and linear discriminant analysis (LDA) [20], aim to reduce the dimensionality of the data by projecting it into a lower-dimensional space. This can help improve recognition accuracy. Finally, deep-learning based methods [23; 24] have recently emerged as a powerful tool for hand-based biometric recognition. These methods have shown promising performance for capturing discriminative features from hand images. Despite recent advancements in hand kinematic analysis, feature extraction approaches still have several limitations. For example, most feature extraction techniques are specific to hand kinematic datasets derived from hand image datasets. Additionally, some techniques are only suitable for specific hand analyses using EMG signals [24; 25] and are difficult to extend to other types of hand kinematic data. Moreover, most of the traditional feature extraction methods are focused on extracting features based on subspace-learning-based approaches while not adequately exploiting the consistency and complementary information among the other types of hand kinematic data, especially hand kinematic time-series formats. To overcome the challenges in hand kinematic analysis, we propose a methodology consisting of three primary steps. First, the hand kinematic dataset undergoes preprocessing through resampling. Next, various feature extraction techniques are applied, including raw data, PCA, sparse coding based on a Gaussian random dictionary, and our proposed sparse coding based on dictionary learning. Finally, the extracted features are used for neural network classification. Our method leverages the sparsity-inducing properties of dictionary learning to capture relevant features from hand kinematics time-series data, as detailed in Figure 1.  
Our proposed method aims to enhance classification accuracy for identifying grasp types using hand kinematics in a time-series format. The proposed feature extraction technique leverages sparse coding based on dictionary learning, as illustrated in Figure 2. Building on existing sparse coding techniques, our approach provides the sparsest solution to an underdetermined linear problem by generating sparse coefficients. The process involves resampling the kinematic dataset to a specific size, vertically concatenating the resampled data, and partitioning the dataset into training and test sets using random partitioning. The training set is used to derive a dictionary through an online dictionary learning algorithm, which is then employed to extract sparse coefficients from both the training and test sets. These sparse coefficients are subsequently used as inputs for neural network classification, as depicted in Figure 3.  
Figure 1: Overview of proposed methodology. Figure 2: The sparse-coding-based feature extraction technique based on dictionary learning. The main contributions of this paper are summarized as follows:  
* We propose a sparse coding technique based on dictionary learning to extract hand kinematic features, which is a context that has not been extensively explored before. Figure 3: Details of proposed method. * Unlike most existing methods that extract features from hand datasets using images, our proposed method demonstrates the potential of using three-dimensional motion tracking using a time-series format for feature extraction. * Our approach differs from our previous work that utilized traditional sparse coding with a dictionary based on a Gaussian random distribution. Instead, we apply a dictionary learning technique to the hand kinematics dataset in a time-series format. Extensive experimental evaluation of the publicly available UNIPI dataset demonstrates the effectiveness of our proposed method compared to existing techniques and our prior work. * A key distinction from previous studies lies in the estimation technique used.