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Processing and interpretation of surface electromyogram signal to design prosthetic device

Published online by Cambridge University Press:  23 September 2014

Karan VeerOpen the ORCID record for Karan Veer[Opens in a new window],
Ravinder Agarwal and
Amod Kumar
Karan Veer*
Affiliation:
Electrical and Instrumentation Engineering Department, Thapar University, Patiala, India
Ravinder Agarwal
Affiliation:
Electrical and Instrumentation Engineering Department, Thapar University, Patiala, India
Amod Kumar
Affiliation:
Central Scientific Instruments Organization (CSIO), Chandigarh, India
*
*Corresponding author. E-mail: karan.una@gmail.com
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Summary

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The study of arm muscles for independent operations leading to prosthetic design was carried out. Feature extraction was done on the recorded signal for investigating the voluntary muscular contraction relationship for different arm motions and then repeated factorial analysis of variance (ANOVA) technique was implemented to analyze effectiveness of signal. The electronic design consisted of analog and digital signal processing and controlling circuit and mechanical assembly consisted of wrist, palm and the fingers to grip the object in addition to a screw arrangement connected to a low power DC motor and gear assembly to open or close the hand. The wrist is mechanically rotated to orient the hand in a direction suitable to pick up/hold the object. The entire set up is placed in a casing which provides a cosmetic appeal to the artificial hand and the connected arm. The design criteria include electronic control, reliability, light weight, variable grip force with ease of attachment for simple operations like opening, grasping and lifting objects of different weight with grip force slightly more than enough just like that of a natural hand.

Keywords

Signal processingBiomechanicsMicrocontrollerElectromyogramVariable grip force

Information

Type
Articles
Information
Robotica , Volume 34 , Issue 7 , July 2016, pp. 1486 - 1494
Copyright
Copyright © Cambridge University Press 2014 

References

1.George, S., Sivanandan, S. K. and Mohandas, P. K., “Fuzzy logic and probabilistic neural network for EMG classification – a comparative study,” Int. J. Eng. Res. Technol. 1, 1–7 (2012).Google Scholar
2.Farfan, D. F., Politti, J. C. and Felice, C. J., “Evaluation of EMG processing techniques using information theory,” Biomed. Eng. Online 9 (72), 1–18 (2010).CrossRefGoogle ScholarPubMed
3.Arora, A. S., Development of Control Strategies for EMG Based Control of Multifunction Prosthetic Hand Ph.D. Thesis (Roorkee: Indian Institute of Technology, 2002) 189–192.Google Scholar
4.Reaz, M. B. I., Hussain, M. S. and Mohd-Yasin, F., “Techniques of EMG signal analysis: Detection, processing, classification and applications,” IEEE Trans. Biomed. Eng. 10, 11–35 (2006).Google Scholar
5.Arora, A. S., “Modified adaptive resonance theory based control strategy for EMG operated prosthesis for below-elbow amputee,” J. Med. Eng. Technol. 31 (3), 191–201 (2007).CrossRefGoogle ScholarPubMed
6.Kumar, N., Kunju, N., Kumar, A. and Sohi, B. S., “Knowledge base generation and its implementation for control of above knee prosthetic device based on SEMG & knee flexion angle,” Int. J. Bio-Mechatron. Biomed. Robot. 1 (2), 126–132 (2010).Google Scholar
7.Tkach, D., Huang, He and Kuiken, A. T., “Study of stability of time-domain features for Electromyographic pattern recognition,” J. NeuroEng. Rehabil. 7 (21), 1–13 (2010).CrossRefGoogle ScholarPubMed
8.Micera, S., “Control of hand prostheses using peripheral information,” IEEE Rev. BME 3, 48–68 (2010).Google ScholarPubMed
9.Merletti, R., Botter, A., Troiano, A., Merlo, E. and Minetto, A. M., “Technology and instrumentation for detection and conditioning of the surface electromyographic signal: State of the art,” Clin. Biomech. 24, 122–134 (2009).CrossRefGoogle ScholarPubMed
10.Ryait, S. H., Arora, S. A. and Ravinder, A., “Interpretation of wrist operations from surface-EMG signals at different locations on arm along with acupressure points,” IEEE Trans. Biomed. Circ. Syst. 4, 101–111 (2010).CrossRefGoogle Scholar
11.Kumar, N., Pankaj, D., Soni, S. and Kumar, A., “Exoskeleton device for rehabilitation of stroke patients using SEMG during isometric contraction,” Adv. Mater. Res. 403–408, 2033–2038 (2012).Google Scholar
12.Tripathy, K. R., Acharya, A., Choudhary, K. S. and Sahoo, K. S., “Artificial neural network based body posture classification from EMG signal analysis,” Indones. J. Electr. Eng. Inform. 1, 59–63 (2013).Google Scholar
13.Lazaro, B. J., Abuan, D. D., Pangani, G. A. and Linsangan, B. N., “Surface electromyogram signal for control of myoelectric prosthesis of upper limb using independent component analysis,” J. Autom. Control Eng. 2, 94–98 (2014).Google Scholar
14.Day, S., Important factors in surface EMG measurement (Bortec Biomedical Ltd, 2009).Google Scholar
15.Jung, K. and Kim, J. W., “EMG Pattern Classification using Spectral Estimation and Neural Network,” Proceedings of the SICE Annual Conference, Kagawa University, Japan (2007) pp. 108–111.Google Scholar
16.Knaflitz, M. and Balestra, G., “Computer analysis of the myoelectric signal,” IEEE Micro. 11, 48–58 (1991).CrossRefGoogle Scholar
17.Kumar, S., Chatterjee, A. and Kumar, A., “Design of a below elbow myoelectric arm with proportional grip force,” J. Sci. Ind. Res. 71, 262–265 (2012).Google Scholar
18.Veer, K., Kumar, S., Agarwal, A. and Kumar, A., “Muscular force based SEMG analysis for above arm using non invasive technique,” In Zonal Seminar on Emerging Trends in Embedded system technologies (ETECH), OP-7, (2013).Google Scholar
19.Kumar, S., Chatterjee, A. and Kumar, A., “Implementation of Proportional Grip Force Based on EMG Signal,” Proceedings of the IUTAM Symposium on Emerging Trend in Rotor Dynamics, Mumbai (2009) pp. 75–76.Google Scholar
20.Coakers, J. S., SPSS Version 12.0 for Windows Analysis Without Anguish (John Wiley Sons Publication, Australia, 2005).Google Scholar