# Feature Extraction Based on Sparse Coding Approach for Hand Grasp Type Classification
## 1 Introduction  
Understanding human hand characteristics involves investigating hand kinematics during various grasping actions and general patterns of hand usage. This research is critical across domains such as medicine, rehabilitation, psychology, and product design. Hand kinematics are studied to understand the characteristics of healthy hand movements [1, 2] and to inform object design based on grasping patterns [3, 4]. Moreover, the study of human hand manipulation has practical applications in the field of robotics, where the versatility of hand kinematics plays a crucial role. To facilitate effective manipulation, human hands exhibit coordination and a multitude of degrees of freedom (DoFs). Therefore, comprehensive hand kinematics data encompassing a wide range of DoFs are essential for studying interactions with various objects under different environmental conditions. Existing methods for analyzing hand kinematics often fail to capture the necessary features for precise classification. The utilization of dimensionality reduction techniques becomes imperative and is implemented with all hand kinematic recording techniques. The dimensionality reduction techniques are used as a pre-processing step and can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction has been performed based on two main methods, which are feature selection and feature extraction. These techniques are utilized for analyzing and interpreting intricate behavior of the human hand [5]. Feature selection involves choosing the most relevant functions that adequatelydescribe the essential indicators. On the other hand, feature extraction entails identifying and transforming the most informative features from a given dataset. Both feature selection and feature extraction are fundamental steps in machine learning models [6], as the quality and relevance of features significantly impact the model's performance and accuracy. Feature extraction becomes a necessary method to identify and extract the essential information so that the investigation of hand kinematics can be made easier. Due to feature extraction outperforming in terms of performance and accuracy, then feature representation is important for analyzing hand kinematics. It involves transforming raw data into a format that highlights essential characteristics, making it easier to identify patterns and perform classification tasks. Accurate feature representation can significantly enhance the performance of machine learning models used for hand kinematic analysis. Hence, this paper addresses this gap by proposing a novel sparse coding feature extraction technique based on dictionary learning. Our contributions are threefold: we introduce a new feature extraction method tailored for hand kinematics time-series data, demonstrate the effectiveness of our method through extensive experimental evaluation, and significantly improve classification accuracy while reducing the number of features compared to existing methods. Existing hand-based feature extraction methods can be divided into four main categories. The first category includes global and local statistical-based methods [7; 8; 9; 10] that use features like local binary histograms and graph structures to capture information from the hand image. Examples of these methods include generalized symmetric local graph structure (GSLGS) [7], local binary pattern (LBP) [8], and histogram of oriented lines (HoL) [9] and its alternatives [10]. These methods are popular for hand-based biometric recognition. Another category is coding-based methods [11; 12; 13; 14; 15], which focus on encoding directional information in the hand image. This information is important because hand patterns often have distinct orientations that are resistant to changes in lighting. Examples of coding-based methods include sparse coding (SC) [15], ordinal code [11], competitive code (Compcode) [12], collaborative representation_Compcode (CR_Compcode) [13], and joint discriminative sparse coding (JDSC) [14].