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Wearable System for Device Control using Bio-Electrical Signal

Affiliations

  • Department of Electronics, Instrumentation and Control Engineering, University of Petroleum and Energy Studies, Dehradun - 248007, Uttarakhand, India
  • Department of Electronics and Communication Engineering, Greater Noida Institute of Technology, Greater Noida - 201306, Uttar Pradesh, India

Abstract


In today’s world, wearable devices are progressively being used for the enhancement of the nature of the life of individuals. Human Machine Interface (HMI) has been studied for dominant the mechanical device rehabilitation aids through biosignals like EOG and EMG etc., and so on. EMG signals have been studied in detail due to the occurrence of a definite signal pattern. The current proposal focuses on the advancement of a Wearable Device control by using EMG signals of hand movements for controlling the electronic devices. EMG signals are utilized for the production of the control indicators to develop the device control. Also, an EMG sign procurement framework was produced. To create different control signals relying on the sufficiency and length of time of signal segments, the obtained EMG signals were then prepared for device control.

Keywords

Electromyography (EMG), HMI, Wrist movements, Wearable.

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References


  • Maria H, Piitulainen H, Visala A. Current state of digital signal processing in myoelectric interfaces and related applications. Biomedical Signal Processing and Control. 2015; 18:334–59.
  • Rechy-Ramirez JE, Hu H. Bio-signal based control in assistive robots: A survey. Digital Communications and Networks. 2015; 1(2):85–101.
  • Jali HM, et al. Joint torque estimation model of sEMG signal for arm rehabilitation device using artificial neural network techniques. Advanced Computer and Communication Engineering Technology. Springer International Publishing, 2015; 315:671–82.
  • Gonzalez-Vargas J, et al. Human-machine interface for the control of multi-function systems based on electrocutaneous menu: application to multi-grasp prosthetic hands. PloS one. 2015; 10:e0127528.
  • Taha Z, et al. IIR filter order and cut-off frequency influences on EMG signal smoothing. Biomedical Research. 2015; 26:616–20.
  • Carey SL, Lura DJ, Highsmith MJ. Differences in myoelectric and body-powered upper-limb prostheses: Systematic literature review. Journal of rehabilitation research and development. 2015; 52(3):247–62.
  • Wang L, et al. Study on upper limb rehabilitation system based on surface EMG. Bio-Medical Materials and Engineering. 2015; 26(s1):795–801.
  • Phinyomark A, Pornchai P, Chusak L. Feature reduction and selection for EMG signal classification. Expert Systems with Applications. 2012; 39(8):7420–31.
  • Reaz MBI, Hussain MS, Mohd-Yasin F. Techniques of EMG signal analysis: Detection, processing, classification and applications. Biological Procedures Online. 2006; 8(1):11–35.
  • Ozgunen KT, Umut C, Kurdak SS. Determination of an optimal threshold value for muscle activity detection in EMG analysis. Journal of Sports Science and Medicine. 2010; 9(4):620.

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