An Innovative Human-Computer Interaction (HCI) for Surface Electromyography (EMG) Gesture Recognition

Authors

  • Arvind Kumar Associate Professor Mechanical Department, Chandigarh Engineering College Jhanjeri Mohali
  • Jayaprakash B. Assistant Professor, Department of Computer Science and IT, School of Sciences, Jain (Deemed-to-be University), Bangalore-27, India
  • Ashendra Kumar Saxena Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Mohit Kumar Sharma Assistant Professor, Department of Electrical Engineeing, Vivekananda Global University, Jaipur, India
  • Archana Verma Assistant Professor, Department of Master of Computer Application, Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India

Keywords:

hand gestures (HG), human-computer interaction (HCI), principal component analysis (PCA), surface electromyogram (SEMG), augmented partial swarm optimization, modified k-nearest neighbor (APSO-MKNN)

Abstract

The interface between citizens and elegant cities is human computer interaction (HCI), a place that is crucial in bridging the application gap for information technology in contemporary cities. Hand gestures (HG) are generally recognized as a potential HCI technique, and the use of Surface Electromyograms (SEMG) to recognize Human Hand Gestures (HHG) is a significant area of study. Modern signal processing techniques, instead, are not robust in feature extraction utilizing Principal Component Analysis (PCA), using feature re-extraction, and guide respect with SEMG signals; there be motionless several technical issues that need to be resolved. The way for instance, can myoelectric control be kept available in intermittent use, as time variability has a significant negative impact on pattern recognition quality yet is unavoidable in regular use. Developing a solid HCI also requires ensuring the myoelectric control system's efficacy and dependability. In this study, Augmented Partial Swarm Optimization and Modified K-Nearest Neighbor (APSO-MKNN) are used in the HGR system that can eliminate redundant information in SEMG signals and increase the effectiveness and precision of recognition. The investigational findings help lower the time differences in Gesture Recognition (GR) based on SEMG.This study is focused on optimizing the time differences in SEMG pattern recognition. The identification approach that is proposed in this study has the possibility of increasing the long-term accuracy of the generalization of an HCI system. Additionally, the proposed framework can simplify the process of data collecting prior to having a gadget prepared and ready for usage.

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References

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Published

11.07.2023

How to Cite

Kumar, A. ., B., J. ., Saxena, A. K. ., Sharma, M. K. ., & Verma, A. . (2023). An Innovative Human-Computer Interaction (HCI) for Surface Electromyography (EMG) Gesture Recognition. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 08–17. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3015

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

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