# Feature Extraction Based on Sparse Coding Approach for Hand Grasp Type Classification
## 3 Sparse Coding Feature Extraction Based on Dictionary Learning Approach
## 7 Conclusions

In this paper, we proposed a novel sparse coding feature extraction technique based on dictionary learning for classifying human hand grasp types. Our method significantly improves classification accuracy and reduces the number of features required compared to PCA- and Gaussian-random-dictionary-based approaches. The classification accuracy of our proposed technique is compared with PCA and sparse coding based on a Gaussian random dictionary. The results from the classification experiments clearly showed that the proposed method is highly effective at classifying hand grasp types. Specifically, the classification accuracy achieved by the proposed scheme is significantly higher than that of PCA-based and other feature extraction techniques. These findings have important implications for various applications, including robotics and rehabilitation. Future research will focus on extending our technique to other types of hand kinematic data and exploring its real-time application potential. Finally, the sparse-coding-based feature extraction based on the dictionary learning approach presented in this paper provides an alternative feature extraction method for classification. Its potential for broader applications makes it a valuable contribution to the field of machine learning (ML). Particularly, in natural language processing (NLP), feature extraction is a fundamental process for converting raw text data into a proper format that can be easily processed by utilizing machine learning algorithms. Conceptualization, J.S. and P.K. ; methodology, J.S., P.K. and N.T.M. ; software, J.S. and P.K. ; validation, M.A.S.K. and I.M. ; formal analysis, J.S. and K.Y. ; investigation, K.Y. ; resources, K.Y. ; data curation, J.S. ; writing--original draft preparation, J.S. ; writing--review and editing, P.K. and K.Y. ; supervision, K.Y. ; project administration, K.Y. ; funding acquisition, K.Y. All authors have read and



## References

* (1) [PERSON]; [PERSON]; [PERSON] [PERSON] Postural hand synergies for tool use. _J. Neurosci._**1998**, _18_, 10105-10115. [CrossRef] [PubMed]
* (2) [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Experimental protocol for the kinematic analysis of the hand: Definition and repeatability. _Gait Posture_**2006**, _23_, 445-454. [CrossRef] [PubMed]
* (3) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Patterns of hand motion during grasping and the influence of sensory guidance. _J. Neurosci._**2002**, _22_, 1426-1435. [CrossRef] [PubMed]
* (4) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Generalization of human grasping for multi-fingered robot hands. In Proceedings of the IEEE International Workshop on Intelligent Robots and Systems (IROS), Vilamoura, Portugal, 7-12 October 2012; pp. 2043-2050. * (5) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] A Machine learning approach for gesture recognition with a lensless smart sensor system. In Proceedings of the IEEE 15 th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Las Vegas, NV, USA, 4-7 March 2018; pp. 4-7. * (6) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition. _J. Pattern Recognit._**2018**, _76_, 80-94. [CrossRef]
* (7) [PERSON]; [PERSON]; [PERSON]; [PERSON] Novel local coding algorithm for multimodal finger feature description and recognition. _Sensors_**2019**, _19_, 2213. [CrossRef] [PubMed]
* (8) [PERSON]; [PERSON]; [PERSON] Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. _IEEE Transaction Pattern Anal. Mach. Intell._**2002**, _24_, 971-987. [CrossRef]
* (9) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Histogram of oriented lines for palmprint recognition. _IEEE Trans. Syst. Man Cybern. Syst._**2014**, _44_ 385-395.