sEMG feature evaluation for identification of elbow angle resolution in graded arm movement

BioMedical Engineering OnLine

Dinesh K Kumar³  Sridhar P Arjunan³  Plinio T Aquino Junior¹  Esther L Colombini²  Maria Claudia F Castro² 

[1]Computer Science Department, Centro Universitário da FEI, Av. Humberto de A. C. Branco, 3.972, São Bernardo do Campo, SP 09850-901, Brazil

[2]Electrical Engineering Department, Centro Universitário da FEI, Av. Humberto de A. C. Branco, 3.972, São Bernardo do Campo, SP 09850-901, Brazil

[3]Biosignal Lab., School of Electrical and Computer Engineering, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia

关键词: Arm flexion/extension;    Angular position;    Feature extraction;    Pattern recognition;    EMG signal;   

Others  :  1084209
DOI  :  10.1186/1475-925X-13-155

 received in 2014-06-26, accepted in 2014-11-17,  发布年份 2014

【 摘 要 】

Automatic and accurate identification of elbow angle from surface electromyogram (sEMG) is essential for myoelectric controlled upper limb exoskeleton systems. This requires appropriate selection of sEMG features, and identifying the limitations of such a system.

This study has demonstrated that it is possible to identify three discrete positions of the elbow; full extension, right angle, and mid-way point, with window size of only 200 milliseconds. It was seen that while most features were suitable for this purpose, Power Spectral Density Averages (PSD-Av) performed best. The system correctly classified the sEMG against the elbow angle for 100% cases when only two discrete positions (full extension and elbow at right angle) were considered, while correct classification was 89% when there were three discrete positions. However, sEMG was unable to accurately determine the elbow position when five discrete angles were considered. It was also observed that there was no difference for extension or flexion phases.

【 授权许可】

   
2014 Castro et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Herr H: Exoeskeleton and orthoses classification, design challenges and future directions. J Neuro Eng Rehabil 2009, 6:21. doi:10.1186/1743-0003-6-21 BioMed Central Full Text
• [2]Lo HS, Xie SQ: Exoskeketon robots for upper limb rehabilitation: state of the art and future prospects. Med Eng Phys 2012, 34:261-268.
• [3]Kiguchi K, Fukuda T: Upper-Limb Exoskeletons for Physically Weak Persons. In Rehabilitation Robotics, August, Chap. 16. Edited by Kommu SS. Viena, Austria: InTech; 2007:287-299. http://www.intechopen.com/books/rehabilitation_robotics/upper-limb_exoskeletons_for_physically_weak_persons webcite.
• [4]Li G: Electromyography Pattern Recognition Based Control of Powered Multifunctional Upper Limb Prostheses. In Advances in Applied Electromyography, Chap. 6. Edited by Mizrahi J. Rijeka, Croatia: InTech; 2011:99-117. http://www.intechopen.com/books/advances-in-applied-electromyography/electromyography-pattern-recognition-based-control-of-powered-multifunctional-upper-limb-prostheses webcite
• [5]Scheme E, Englehart K: Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. J Rehabil Res Dev 2011, 48(6):643-660.
• [6]Lenzi T, De Rossi S, Vitiello N, Carrozza M: Intention-based EMG control for powered exoskeletons. IEEE Trans Biomed Eng 2012, 59(8):2180-2190.
• [7]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):1064-1071.
• [8]Zhang X, Zhou P: High-density myoelectric pattern recognition toward improved stroke rehabilitation. IEEE Trans Biomed Eng 2012, 59(6):1649-1657.
• [9]Muceli S, Farina D: Simultaneous and proportional estimation of hand kinematics from emg during mirrored movements at multiple degrees-of-freedom. IEEE Trans Neural Syst Rehabil Eng 2012, 20(3):371-378.
• [10]Hudgins B, Parker P, Scott RN: A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng 1993, 40(1):82-94.
• [11]Englehart K, Hudgins B, Parker PA, Stevenson M: Classification of the myoelectric signal using time-frequency based representations. Med Eng Phys 1999, 21(6–7):431-438.
• [12]Liu X, Zhou R, Yang L, Li G: Performance of Various EMG Features in Identifying arm Movements for Control of Multifunctional Prostheses. In Proceedings of the IEEE Youth Conference on Information Computing and Telecommunication. Beijing: IEEE; 2009:287-290.
• [13]Oskoei MA, Hu H: GA-Based Feature Subset Selection for Myoelectric Classification. In Proceedings of the IEEE International Conference on Robotics and Biomimetics. Kunming, China: IEEE; 2006:1465-1470.
• [14]Du S, Vuskovic M: Temporal vs. Spectral Approach to Feature Extraction from Prehensile EMG Signals. In Proceedings of the IEEE International Conference on Information Reuse and Integration-IRI. Las Vegas, USA: IEEE; 2004:344-350.
• [15]Artemiadis PK, Kyriakopoulos KJ: An EMG-based robot control scheme robust to time-varying EMG signal features. IEEE Trans Inf Technol Biomed 2010, 14(3):582-588.
• [16]Alkan A, Gunay M: Identification of EMG signals using discriminant analysis and SVM classifier. Expert Syst Appl 2012, 39(1):44-47.
• [17]Farfán FD, Politti JC, Felice CJ: Evaluation of EMG processing techniques using information theory. BioMedical Eng Online 2010, 9:72. http://www.biomedical-engineering-online.com/content/9/1/72 webcite BioMed Central Full Text
• [18]Khokhar ZO, Xiao ZG, Menon C: Surface EMG pattern recognition for real-time control of a wrist exoskeleton. BioMedical Eng Online 2010, 9:41. http://www.biomedical-engineering-online.com/content/9/1/41 webcite BioMed Central Full Text
• [19]Bunderson NE, Kuiken TA: Quantification of feature space changes with experience during electromyogram pattern recognition control. IEEE Trans Neural Syst Rehabil Eng 2012, 20(3):239-246.
• [20]Belhumeur P, Hespanha P, Kriegman D: Eigenfaces vs fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 1997, 19(7):711-720.
• [21]Thomaz CE, Gillies DF: A Maximum Uncertainty LDA-Based Approach for Limited Sample Size Problems With Application to Face Recognition. In Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing-SIBGRAPI’05. Natal, Brazil: IEEE; 2005:89-96.
• [22]Castro MCF: Statistical Approach for Angular Position Separability Classes of EMG Data. In Proceedings of the ISSNIP Biosignals and Biorobotics Conference-BRC. Vitória, Brazil: IEEE; 2011:1-6. doi:10.1109/BRC.2011.5740663
• [23]Garrett JA, Viera JM: Understanding interobserver agreement: the kappa statistic. Fam Med 2005, 37(5):360-363.