# Feature Extraction Based on Sparse Coding Approach for Hand Grasp Type Classification
## 3 Sparse Coding Feature Extraction Based on Dictionary Learning Approach
The fourth row features a screw used for a pinch grasp. Finally, in the last row, a marker is utilized for reorientation in a rotation grasp. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Object** & **Hand Grasp Type** \\ \hline
1 & 2-Euro Coin & \\
2 & Button Badge & Flip \\
3 & Key & \\ \hline
4 & Credit Card & \\
5 & CD & Edge \\
6 & Hair-Coloring Comb & \\ \hline
7 & Saltshaker & \\
8 & Tape & \\
9 & Chess (Queen) & Closing \\
10 & Knob & \\
11 & Matchbox & \\ \hline
12 & Screw & \\
13 & Match & Pinch \\
14 & Cigarette & \\
15 & Rubber Band & \\ \hline
16 & Maker & \\
17 & Screwdriver & \\
18 & Shashlik & Rotation \\
19 & Glasses & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The details of 19 objects and hand grasp types. The kinematics model of the human hand has complexity that needs to be described and investigated. In this work, the kinematics model of the human hand is considered based on 20 degrees of freedom, as illustrated in Figure 5. Four long fingers, consisting of the index, middle, ring, and little, are described by four angles. Each long finger is described based on motions of the joint that have two DoFs at the metacarpophalangeal joints to describe flexion-extension and abduction-adduction mobilities along with one DoF at the proximal and distal interphalangeal joints to describe flexion-extension mobilities. The thumb is represented based on motions of four joints that have two DoFs at the trapeziometacarpal for expressing flexion-extension and abduction-adduction mobilities, along with one DoF at the metacarpophalangeal joints, and one DoF at the interphalangeal joint to describe flexion-extension mobility. The kinematic structure of the human hand model is described by Denavit-Hartenberg (DH) parameters to express the kinematic chain of each finger [35]. The description of hand kinematics based on 20 DoFs is described in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **DoFs** & **Description** \\ \hline
1 & TA & Thumb Abduction \\
2 & TR & Thumb Rotation \\
3 & TM & Thumb Metacarpal \\
4 & TI & Thumb Interphalangeal \\
5 & IA & Index Abduction \\
6 & IM & Index Metacarpal \\
7 & IP & Index Proximal \\
8 & ID & Index Distal \\
9 & MA & Middle Abduction \\
10 & MM & Middle Metacarpal \\
11 & MP & Middle Proximal \\
12 & MD & Middle Distal \\
13 & RA & Ring Abduction \\
14 & RM & Ring Metacarpal \\
15 & RP & Ring Proximal \\
16 & RD & Ring Distal \\
17 & LA & Little Abduction \\
18 & LM & Little Metacarpal \\
19 & LP & Little Proximal \\
20 & LD & Little Distal \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of degrees of freedom. Figure 4: Sequences to illustrate five grasp types. ### Results for the UNIPI Dataset

The UNIPI dataset contributed hand kinematics data. The dataset is divided into 19 different objects, with each object subjected to 2 separate trials. In the first step, we applied data processing techniques to ensure that the kinematic data were in a suitable time-series format by adjusting for differences in data length. This involved resampling processes to effectively format the data by utilizing linear interpolation to adjust the length of the dataset to a specific size (500 time series). Before the next process, the resampled data were utilized to construct training and test sets using random partitions for holdout cross-validation. The training dataset comprised approximately 80% of the kinematic data, while the validation dataset accounted for the remaining 20%. The second step focused on feature extraction, where we employed the proposed technique based on dictionary learning constructed from the training set. The necessary parameters to construct the learned dictionary as \(k\) is prescribed as 70, and the number of iterations \(T\) was 100 iterations. The learned dictionary was utilized to obtain sparse coefficients based on sparse decomposition from the training and test sets.