Finger Movement Identification Using EMG Signal on the Forearm

Journal Title: Journal of Biomedical Engineering and Medical Imaging - Year 2017, Vol 4, Issue 4

Abstract

Finger movement identification is an important innovative interfacing method which has countless possible applications. It can be used to create a new age in human computer interfacing (HCI) devices. It can also be applied to medical applications, such as in the development of a more advanced prosthetic hand. The current research for this purpose includes methods such as computer vision and detecting finger motion through mechanical vibrations from skin surface. They have the limitation of being restrictive, in terms of the degree of movement that the hand is allowed from a certain optimum position, as well as being susceptible to environmental factors. In this study, the surface electromyography (sEMG) of the forearm from skin electrodes is developed and interfaced with computer. The response at the flexor carpi radialis muscle of the forearm is plotted for a group of subjects to observe the qualitative responsiveness of the sEMG to different types of finger movements. The results show that finger movement generates a corresponding response on the EMG electrodes. For the particular muscle being studied, the greatest individual digit amplitude response was observed for the ring finger (digitus annularis) across the subjects. In future studies, this research could be made more quantitative in nature by observing the frequency content of a variety of hand gestures across a sample of subjects.

Authors and Affiliations

N Sheikh, F. Muhammad, M. F. Shamim, N. Shahid, S. M. Omair, M. Z. Ul Haque

Keywords

Related Articles

Approach to Detecting Forest Fire by Image Processing Captured from IP Cameras

In this paper, the results show an algorithm to detect the presence of smoke and flame using image sequences captured by Internet Protocol (IP) cameras is represented. The important characteristics of smoke such as color...

Hybrid Algorithm Edge Detected DICOM Image Enhancement and Analysis based on Genetic Algorithm for Evolution and Best Fit Value

The segmentation of a DICOM standard medical image is a necessary technique which is essential for feature extraction, object edge detection and classification of the segments of the image. The DICOM image is partitioned...

Surface Vs Volume Based Reconstruction of Bone Tissue Using CAS_Annotate and CAS_Navigate

Intra-operative systems that provide 3D spatial reasoning support, require 3D models whose geometric accuracy enables the surgeon to make relative positioning and orientation decisions of anatomical structures during nav...

Improved Fuzzy C-Means Algorithm for Brain Tumor Identification Analysis Using Magnetic Resonance Brain Images

Image processing plays a very important role in the analysis images of different standards; it supports the doctor’s decision and helps to easily diagnose the patient. In this paper we processed the magnetic resonance br...

A Novel Two-Stage Thresholding Method for Segmentation of Malaria Parasites in Microscopic Blood Images

Developing computerized diagnostic tool for the detection of malaria infected cells in microscopic blood images can help to reduce malaria-induced mortality. Segmentation of malaria infected cells is a key step in the au...

Download PDF file
  • EP ID EP283571
  • DOI 10.14738/jbemi. 44.3528
  • Views 80
  • Downloads 0

How To Cite

N Sheikh, F. Muhammad, M. F. Shamim, N. Shahid, S. M. Omair, M. Z. Ul Haque (2017). Finger Movement Identification Using EMG Signal on the Forearm. Journal of Biomedical Engineering and Medical Imaging, 4(4), 12-19. https://europub.co.uk/articles/-A-283571