nach oben

Medical & Biological Engineering & Computing

Erschienen in:

04.01.2017 | Original Article

Ranking hand movements for myoelectric pattern recognition considering forearm muscle structure

verfasst von: Youngjin Na, Sangjoon J. Kim, Sungho Jo, Jung Kim

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 8/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Previous pattern recognition algorithms using surface electromyography (sEMG) have been developed for subsets of predefined hand movements without considering muscle structure. In order to decode hand movements, it is important to know which movements are appropriate for PR due to the different independence of movements between individuals and the high correlated characteristics of sEMG patterns between movements. This paper proposes a method to personally rank the order of hand movements from subsets (31 finger flexion, 31 finger extension, and 4 wrist movements in this paper). The movements were sorted into a ranked order with respect to the locations of the electrodes on the proximal forearm and the distal forearm. We evaluated the classification error as the number of desired movements (N _m) changed. The maximum N _m with an error lower than 10% was 20 for the proximal forearm and 10 for the distal forearm from ranked movements of individuals. Our method could help to identify the optimized order of hand movements considering the personal characteristics of each individual.

Vorheriger Artikel A robust approach for exploring hemodynamics and thrombus growth associations in abdominal aortic aneurysms

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Al-Timemy AH, Escudero J, Bugmann G, Outram N (2013) Protocol for site selection and movement assessment for the myoelectric control of a multi-functional upper-limb prosthesis. Annu Int Conf IEEE Eng Med Biol Soc 2013:5817–5820

Al-Timemy AH, Bugmann G, Escudero J, Outram N (2013) Classification of finger movements for the dexterous hand prosthesis control with surface electromyography. IEEE J Biomed Health Inform 17(3):608–618CrossRefPubMed

Al-Timemy A, Khushaba R, Bugmann G, Escudero J (2015) Improving the performance against force variation of EMG controlled multifunctional upper-limb prostheses for transradial amputees. IEEE Trans Neural Syst Rehabil Eng 24(6):650–651

Amsüss S, Goebel PM, Jiang N, Graimann B, Paredes L, Farina D (2014) Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control. IEEE Trans Biomed Eng 61(4):1167–1176CrossRefPubMed

Artemiadis PK, Kyriakopoulos KJ (2010) An EMG-based robot control scheme robust to time-varying EMG signal features. IEEE Trans Inf Technol Biomed 14(3):582–588CrossRefPubMed

Artemiadis PK, Kyriakopoulos KJ (2011) A switching regime model for the EMG-based control of a robot arm. IEEE Trans Syst Man Cybern Part B Cybern 41(1):53–63CrossRef

Bunderson NE, Kuiken TA (2012) Quantification of feature space changes with experience during electromyogram pattern recognition control. IEEE Trans Neural Syst Rehabil Eng 20(3):239–246CrossRefPubMed

Cesqui B, Tropea P, Micera S, Krebs HI (2013) EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: a feasibility study. J Neuroeng Rehabil 10:75CrossRefPubMedPubMedCentral

Chattopadhyay R, Jesunathadas M, Poston B, Santello M, Ye J, Panchanathan S (2012) A subject-independent method for automatically grading electromyographic features during a fatiguing contraction. IEEE Trans Biomed Eng 59(6):1749–1757CrossRefPubMedPubMedCentral

10.

Choi E, Lee C (2003) Feature extraction based on the Bhattacharyya distance. Pattern Recognit 36(8):1703–1709CrossRef

11.

Cipriani C, Antfolk C, Controzzi M, Lundborg G, Rosen B, Carrozza MC, Sebelius F (2011) Online myoelectric control of a dexterous hand prosthesis by transradial amputees. IEEE Trans Neural Syst Rehabil Eng 19(3):260–270CrossRefPubMed

12.

De Luca CJ, Gilmore LD, Kuznetsov M, Roy SH (2010) Filtering the surface EMG signal: movement artifact and baseline noise contamination. J Biomech 43(8):1573–1579CrossRefPubMed

13.

Ertuğrul ÖF, Kaya Y, Tekin R (2015) A novel approach for SEMG signal classification with adaptive local binary patterns. Med Biol Eng Comput 54(7):1137–1146CrossRefPubMed

14.

Fougner A, Scheme E, Chan ADC, Englehart K, Stavdahl Ø (2011) Resolving the limb position effect in myoelectric pattern recognition. IEEE Trans Neural Syst Rehabil Eng 19(6):644–651CrossRefPubMed

15.

Häger-Ross C, Schieber MH (2000) Quantifying the independence of human finger movements: comparisons of digits, hands, and movement frequencies. J Neurosci 20(22):8542–8550PubMed

16.

Han H, Jo S (2013) Supervised hierarchical Bayesian model-based electomyographic control and analysis. IEEE J Biomed Health Inform 18(4):1214–1224CrossRefPubMed

17.

He J, Zhang D, Sheng X, Li S, Zhu X (2014) Invariant surface EMG feature against varying contraction level for myoelectric control based on muscle coordination. IEEE J Biomed Health Inform 19(3):874–882CrossRef

18.

Ives JC, Wigglesworth JK (2003) Sampling rate effects on surface EMG timing and amplitude measures. Clin Biomech 18(6):543–552CrossRef

19.

Jung H, Ko C, Kim JS, Lee B, Lim D (2015) Alterations of relative muscle contribution induced by hemiplegia: straight and turning gaits. Int J Precis Eng Manuf 16(10):2219–2227CrossRef

20.

Khushaba RN, Kodagoda S, Takruri M, Dissanayake G (2012) Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Syst Appl 39(12):10731–10738CrossRef

21.

Kiguchi K, Hayashi Y (2012) An EMG-based control for an upper-limb power-assist exoskeleton robot. IEEE Trans Syst Man Cybern Part B Cybern 42(4):1064–1071CrossRef

22.

Kilbreath SL, Gandevia SC (1994) Limited independent flexion of the thumb and fingers in human subjects. J Physiol 479(Pt 3):487–497CrossRefPubMedPubMedCentral

23.

Lee SW, Wilson KM, Lock BA, Kamper DG (2011) Subject-specific myoelectric pattern classification of functional hand movements for stroke survivors. IEEE Trans Neural Syst Rehabil Eng 19(5):558–566CrossRefPubMed

24.

Lee S, Kim H, Jeong H, Kim J (2015) Analysis of musculoskeletal system of human during lifting task with arm using electromyography. Int J Precis Eng Manuf 16(2):393–398CrossRef

25.

Matrone GC, Cipriani C, Carrozza MC, Magenes G (2012) Real-time myoelectric control of a multi-fingered hand prosthesis using principal components analysis. J Neuroeng Rehabil 9(1):40CrossRefPubMedPubMedCentral

26.

Micera S, Carpaneto J, Raspopovic S (2010) Control of hand prostheses using peripheral information. IEEE Rev Biomed Eng 3:48–68CrossRefPubMed

27.

Momen K, Krishnan S, Chau T (2007) Real-time classification of forearm electromyographic signals corresponding to user-selected intentional movements for multifunction prosthesis control. IEEE Trans Neural Syst Rehabil Eng 15(4):535–542CrossRefPubMed

28.

Naik G, Nguyen H (2015) Non negative matrix factorization for the identification of EMG finger movements: evaluation using matrix analysis. IEEE J Biomed Health Inform 19(2):478–485

29.

Naik GR, Baker KG, Nguyen HT (2014) Dependency independency measure for posterior and anterior EMG sensors used in simple and complex finger flexion movements: evaluation using SDICA. IEEE J Biomed Health Inform 19(5):1689–1696

30.

Naik GR, Member S, Al-Timemy A, Nguyen HT (2016) Transradial amputee gesture classification using an optimal number of sEMG sensors: an approach using ICA clustering. IEEE Trans Neural Syst Rehabil Eng 24(8):837–846CrossRefPubMed

31.

Oskoei MA, Hu H (2007) Myoelectric control systems—a survey. Biomed Signal Process Control 2(4):275–294CrossRef

32.

Ouyang G, Zhu X, Ju Z, Liu H (2014) Dynamical characteristics of surface EMG signals of hand grasps via recurrence plot. IEEE J Biomed Health Inform 18(1):257–265CrossRefPubMed

33.

Scheme E, Englehart K (2011) Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. J Rehabil Res Dev 48(6):643–659CrossRefPubMed

34.

Scheme E, Lock B, Hargrove L, Hill W, Kuraganti U, Englehart K (2013) Motion normalized proportional control for improved pattern recognition based myoelectric control. IEEE Trans Neural Syst Rehabil Eng 22(1):149–157CrossRefPubMed

35.

Shi J, Cai Y, Zhu J, Zhong J, Wang F (2013) SEMG-based hand motion recognition using cumulative residual entropy and extreme learning machine. Med Biol Eng Comput 51(4):417–427CrossRefPubMed

36.

Tenore FVG, Ramos A, Fahmy A, Acharya S, Etienne-Cummings R, Thakor NV (2009) Decoding of individuated finger movements using surface electromyography. IEEE Trans Biomed Eng 56(5):1427–1434CrossRefPubMed

37.

Wang G, Ren D (2013) Classification of surface electromyographic signals by means of multifractal singularity spectrum. Med Biol Eng Comput 51(3):277–284CrossRefPubMed

38.

Young AJ, Hargrove LJ, Kuiken TA (2012) Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration. IEEE Trans Biomed Eng 59(3):645–652CrossRefPubMed

39.

Young AJ, Smith LH, Rouse EJ, Hargrove LJ (2013) Classification of simultaneous movements using surface EMG pattern recognition. IEEE Trans Biomed Eng 60(5):1250–1258CrossRefPubMed

40.

Yu H-L, Chase RA, Strauch B (2003) Atlas of hand anatomy and clinical implications. Mosby, St. Louis

Titel: Ranking hand movements for myoelectric pattern recognition considering forearm muscle structure
verfasst von: Youngjin Na
Sangjoon J. Kim
Sungho Jo
Jung Kim
Publikationsdatum: 04.01.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Medical & Biological Engineering & Computing / Ausgabe 8/2017
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-016-1608-4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 8/2017

Sparse representation-based EMD and BLDA for automatic seizure detection

Considerations in applying compressed sensing to in vivo phosphorus MR spectroscopic imaging of human brain at 3T

Stress analysis of fracture of atherosclerotic plaques: crack propagation modeling

Patient-specific 3D hemodynamics modelling of left coronary artery under hyperemic conditions

A novel approach for local abdominal aortic aneurysm growth quantification

Coupling of shear–circumferential stress pulses investigation through stress phase angle in FSI models of stenotic artery using experimental data