# Feature Extraction Based on Sparse Coding Approach for Hand Grasp Type Classification
## 3 Sparse Coding Feature Extraction Based on Dictionary Learning Approach
## 4 Neural Network Classification

Neural network classification was utilized to evaluate the efficiencies of the proposed feature extraction. The UNIPI dataset contains hand kinematics data and is composed of 2 trials from 6 participants and involves 19 objects, and it was subjected to training using the \"fitcnet\" function within the MATLAB 2022b and classified into five grasping types. This training process employed a feedforward neural network for classification purposes. The initial fully connected layer of the model was linked to the predictor data, with each subsequent sublayer connected to the preceding one. Within each layer, the input was multiplied by a weight matrix and subsequently had a bias vector added to it. Furthermore, the rectified linear unit (ReLU) function served as the activation function. The limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithm was employed as the parameter estimation solver. In the final layer, the softmax function was applied as the activation function to generate the output, which consisted of classification scores and predicted labels. A summary of all hyperparameters used in the proposed method can be found in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Layer size & 40 \\ Cost function & Cross-entropy \\ Activation function & Rectified linear unit (ReLU) \\ Output classifier & Softmax function \\ Parameter estimation solver & Limited-memory Broyden–Fletcher–Goldfarb–Shanno \\ Regularization parameter (lambda) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters of NN classification. ## 5 Experiments

A kinematic hand dataset in a time-series format was utilized to demonstrate the effectiveness of the proposed method. The details of the dataset are described in this section. The dataset contains recordings of hand movements involving 21 objects and can be categorized into 5 grasp types, as shown in Table 2. The classification results of these five grasp types were used to evaluate the performance of the proposed method, which is also detailed in this section. All experiments were conducted on a MacBook Pro (14-inch, 2021) running macOS, equipped with an Apple M1 Pro 8-core CPU and 16 GB of unified memory, and using MATLAB 2022b. ### Dataset

The UNIPI dataset [35] provides hand kinematics data based on visual sensors to investigate postural synergies of human grasping by six subjects during asked notification to grasp objects that can be described as a hand posture behavior from a hand kinematic perspective. Regarding the participants, six volunteers were invited to record and test in the experiments. The six volunteers, including three females and three males, were 23-27 years old and had a mean age of 25.17 years. All volunteers were tested in the experiment based on self-reported right-hand dominance. All volunteers had no neuromuscular disorders that affected the investigation's experimental purpose. Before the experiment, all volunteers signed a consent form to participate in the investigation. The acquisition setup consisted of two significant acquisition sources. First was the PhaseSpace Motion Capture system, which records kinematics data based on three-dimensional motion tracking with active LED markers. The system includes stereo cameras for tracking the 3D positions of active LED markers, which are attached to the volunteer's hand and phalanges. The second source was the set of 21 objects that were selected. In this dataset, two objects were displaced as we could not clarify the hand grasp types. Then, we considered only 19 objects and clarified them into 5 grasp types as shown in Table 2. Figure 4 depicts sequences of examples illustrating various grasping types. In the first row, the hand employs a key to execute a flip-type grasp. Moving to the second row, a credit card is utilized for a sliding grasp: guiding it across the surface to the edge of the table. In the third row, tape is employed for a robust grasp, demonstrating a closing grasp.