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A real time surface electromyography signal driven prosthetic hand model using PID controlled DC motor

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An Erratum to this article was published on 23 January 2017

Abstract

Purpose

A surface electromyography (sEMG) driven proportional-integral-derivative (PID) control method is proposed to control the prosthetic hand model according to human intentions in real time.

Methods

The sEMG signals are acquired from the biceps and triceps brachii muscles of the human hand from 30 able bodied subjects. Two time domain features, integrated EMG (IEMG) and number of zero crossing (ZC) are extracted from the sEMG signals and these features are used for the estimation of human forearm kinematics. The estimation of human forearm kinematics is achieved by multi layered perceptron neural network (MLPNN) model based on nonlinear auto regressive with exogenous (NARX) inputs. The estimated human kinematics are utilized to control a direct current (DC) motor based prosthetic hand model using PID controller. The controller parameters are tuned manually to obtain the best possible results.

Results

It is observed that the IEMG and ZC varies with change in angular displacement and also with change in angular velocity. The performance of estimation and control is evaluated using two statistical parameters, root mean square error (RMSE) and regression value. The RMSE and regression value obtained during estimation of angular displacement is 5.89 and 0.97 and the corresponding value obtained for the estimation of angular velocity is 18.91 and 0.80. The RMSE and regression value obtained during control of angular displacement is 18.9096 and 0.9456 and the corresponding value obtained for the control of angular velocity is 27.91 and 0.68.

Conclusions

Experimental results confirm that the estimation using MLPNN and PID controlled prosthetic DC motor hand model performs well. The proposed method is simple in design and can be implemented in a human being with fewer costs.

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References

  1. Englehart K, Hudgins B. A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans Biomed Eng. 2003; 50(7):848–54.

    Article  Google Scholar 

  2. Robot PE, Kiguchi K, Hayashi Y. An EMG-based control for an upper-limb power-assist exoskeleton robot. IEEE Trans Syst Man Cybern B Cybern. 2012; 42(4):1064–71.

    Article  Google Scholar 

  3. Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Syst Appl. 2012; 39(8):7420–31.

    Article  Google Scholar 

  4. Au AT, Kirsch RF. EMG-based prediction of shoulder and elbow kinematics in able-bodied and spinal cord injured individuals. IEEE Trans Rehabil Eng. 2000; 8(4):471–80.

    Article  Google Scholar 

  5. Khushaba RN, Kodagoda S, Takruri M, Dissanayake G. Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Syst Appl. 2012; 39(12):10731–8.

    Article  Google Scholar 

  6. Sapsanis C, Georgoulas G, Tzes A. EMG based classification of basic hand movements based on time-frequency features. IEEE Int Conf Control Autom. 2009; doi:10.1109/MED.2013.6608802.

    Google Scholar 

  7. Dimitrov GV, Arabadzhiev TI, Mileva KN, Bowtell JL, Crichton N, Dimitrova NA. Muscle fatigue during dynamic contractions assessed by new spectral indices. Med Sci Sports Exerc. 2006; 38(11):1971–9.

    Article  Google Scholar 

  8. Venugopal G, Navaneethakrishna M, Ramakrishnan S. Extraction and analysis of multiple time window features associated with muscle fatigue conditions using sEMG signals. Expert Syst Appl. 2014; 41(6):2652–9.

    Article  Google Scholar 

  9. Subasi A. Classification of EMG signals using combined features and soft computing techniques. Appl Soft Comput. 2012; 12(8):2188–98.

    Article  Google Scholar 

  10. Buchanan TS, Lloyd DG, Manal K, Besier TF. Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command. J Appl Biomech. 2006; 20(4):367–95.

    Article  Google Scholar 

  11. Han J, Ding Q, Xiong A, Zhao X. A state-space EMG model for the estimation of continuous joint movements. IEEE T Ind Electron. 2015; 62(7):4267–75.

    Article  Google Scholar 

  12. Jang G, Kim J, Choi Y, Yim J. Human shoulder motion extraction using EMG signals. Int J Precis Eng Man. 2014; 15(10):2185–92.

    Article  Google Scholar 

  13. Jalaludin NA, Sidek SN, Shamsudin AU. Neuro-based thumbtip force and joint angle modelling for development of prosthetic thumb control. Int J Adv Robot Syst. 2013; 10(10):339.

    Google Scholar 

  14. Lee S, Oh J, Kim Y, Kwon M, Kim J. Estimation of the upper limb lifting movement under varying weight and movement speed. Conf Proc Int J Eng Ind. 2011; 97–105.

    Google Scholar 

  15. Yu HJ, Lee AY, Choi Y. Human elbow joint angle estimation using electromyogram signal processing. IET Signal Process. 2011; 5(8):767–75.

    Article  MathSciNet  Google Scholar 

  16. Caldwell DG, Medrano-Cerda GA, Goodwin M. Control of pneumatic muscle actuators. IEEE Contr Syst Mag. 1995; 15(1):40–8.

    Article  Google Scholar 

  17. Thanh TDC, Ahn KK. Nonlinear PID control to improve the control performance of 2 axes pneumatic artificial muscle manipulator using neural network. Mechatronics. 2006; 16(9):577–87.

    Article  Google Scholar 

  18. De Luca CJ, Gilmore LD, Kuznetsov M, Roy SH. Filtering the surface EMG signal: movement artifact and baseline noise contamination. J Biomech. 2010; 43(8):1573–9.

    Article  Google Scholar 

  19. Hof AL, Elzinga H, Grimmius W, Halbertsma JP. Speed dependence of averaged EMG profiles in walking. Gait Posture. 2002; 16(1):78–86.

    Article  Google Scholar 

  20. Karthick PA, Venugopal G, Ramakrishnan S. Analysis of muscle fatigue progression using cyclostationary property of surface electromyography signals. J Med Syst. 2016; 40(1):28.

    Article  Google Scholar 

  21. Potvin JR, Bent LR. A validation of techniques using surface EMG signals from dynamic contractions to quantify muscle fatigue during repetitive tasks. J Electromyogr Kinesiol. 1997; 7(2):131–9.

    Article  Google Scholar 

  22. Venugopal G, Ramakrishnan S. Analysis of progressive changes associated with muscle fatigue in dynamic contraction of biceps brachii muscle using surface EMG signals and bispectrum features. Biomed Eng Lett. 2014; 4(3):269–76.

    Article  Google Scholar 

  23. Doheny EP, Lowery MM, Fitzpatrick DP, O’Malley MJ. Effect of elbow joint angle on force-EMG relationships in human elbow flexor and extensor muscles. J Electromyogr Kinesiol. 2008; 18(5):760–70.

    Article  Google Scholar 

  24. Huang Y, Englehart KB, Hudgins B, Chan AD. A gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses. IEEE Trans Biomed Eng. 2005; 52(11):1801–11.

    Article  Google Scholar 

  25. Pan L, Zhang D, Liu J, Sheng X, Zhu X. Continuous estimation of finger joint angles under different static wrist motions from surface EMG signals. Biomed Signal Proces. 2014; 14(1):265–71.

    Article  Google Scholar 

  26. Boostani R, Moradi MH. Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiol Meas. 2003; 24(2):309–19.

    Article  Google Scholar 

  27. Menezes JMP, Barreto GA. Long-term time series prediction with the NARX network: an empirical evaluation. Neurocomputing. 2008; 71(16-18):3335–43.

    Article  Google Scholar 

  28. Darus IZM, Al-Khafaji AAM. Non-parametric modelling of a rectangular flexible plate structure. Eng Appl Artif Intel. 2012; 25(1):94–106.

    Article  Google Scholar 

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Correspondence to Retheep Raj.

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An erratum to this article is available at http://dx.doi.org/10.1007/s13534-017-0011-x.

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Raj, R., Ramakrishna, R. & Sivanandan, K.S. A real time surface electromyography signal driven prosthetic hand model using PID controlled DC motor. Biomed. Eng. Lett. 6, 276–286 (2016). https://doi.org/10.1007/s13534-016-0240-4

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  • DOI: https://doi.org/10.1007/s13534-016-0240-4

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