Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Generation of Search Behavior by a Modification of Q-MDP Value Method Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

GMM-Based Single-Joint Angle Estimation Using EMG Signals

verfasst von : Stefano Michieletto, Luca Tonin, Mauro Antonello, Roberto Bortoletto, Fabiola Spolaor, Enrico Pagello, Emanuele Menegatti

Erschienen in: Intelligent Autonomous Systems 13

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

This paper aims to explore the possibility to use Electromyography (EMG) to train a Gaussian Mixture Model (GMM) in order to estimate the bending angle of a single human joint. In particular, EMG signals from eight leg muscles and the knee joint angle are acquired during a kick task from three different subjects. GMM is validated on new unseen data and the classification performances are compared with respect to the number of EMG channels and the number of collected trials used during the training phase. Achieved results show that our framework is able to obtain high performances even using few EMG channels and with a small training dataset (Normalized Mean Square Error: 0.96, 0.98, 0.98 for the three subjects, respectively), opening new and interesting perspectives for the hybrid control of humanoid robots and exoskeletons.

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
Vorheriges Kapitel Anthropomorphic Finger Mechanism with a Nonelastic Branching Tendon
Nächstes Kapitel Human Muscle-Tendon Stiffness Estimation During Normal Gait Cycle Based on Gaussian Mixture Model
Fußnoten
1
WMA Declaration of Helsinki—Ethical Principles for Medical Research Involving Human Subjects http://​www.​wma.​net/​en/​30publications/​10policies/​b3/​.
 
2
BTS S.p.A., Milan, Italy.
 
3
MATLAB, The MathWorks, Inc., Natick, Massachusetts, United States.
 
Literatur
1.
Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: 2nd International Symposium on Information Theory. pp. 267–281 (1973)
2.
Artemiadis, P.K., Kyriakopoulos, K.J.: A bio-inspired filtering framework for the EMG-based control of robots. In: IEEE 17th Mediterranean Conference on Control and Automation. pp. 1155–1160 (2009)
3.
Arvetti, M., Gini, G., Folgheraiter, M.: Classification of EMG signals through wavelet analysis and neural networks for controlling an active hand prosthesis. In: IEEE 10th International Conference on Rehabilitation Robotics. pp. 531–536 (2007)
4.
Barron, A., Rissanen, J., Yu, B.: The minimum description length principle in coding and modeling. IEEE Transactions on Information Theory 44(6), 2743–2760 (1998)
5.
Calinon, S., Billard, A.: Active Teaching in Robot Programming by Demonstration. Robot & Human Interactive Communication pp. 702–707 (2007)
6.
Calinon, S., Billard, A.: A probabilistic Programming by Demonstration framework handling constraints in joint space and task space. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 367–372 (2008)
7.
Chowdhury, R., Reaz, M., Ali, M., Bakar, A., Chellappan, K., Chang, T.: Surface electromyography signal processing and classification techniques. Sensors 13(9), 12431–12466 (2013)
8.
Chu, J., Lee, Y.J.: Conjugate prior penalized learning of Gaussian mixture models for EMG pattern recognition. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 1093–1098 (2007)
9.
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) pp. 1–38 (1977)
10.
Englehart, K., Hudgins, B., Parker, P.A., Stevenson, M.: Classification of the myoelectric signal using time-frequency based representations. Medical Engineering & Physics 21(6–7), 431–438 (1999)
11.
Fukuda, O., Tsuji, T., Kaneko, M., Otsuka, A.: A human-assisting manipulator teleoperated by EMG signals and arm motions. IEEE Transactions on Robotics and Automation 19 (2003)
12.
Gopura, R., Kiguchi, K., Li, Y.: SUEFUL-7: A 7DOF upper-limb exoskeleton robot with muscle-model-oriented EMG-based control. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 1126–1131 (2009)
13.
Khokhar, Z.O., Xiao, Z.G., Menon, C.: Surface EMG pattern recognition for real-time control of a wrist exoskeleton. Biomedical engineering online 9, 41 (2010)
14.
Kiguchi, K., Hayashi, Y.: An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot. IEEE Transactions on Systems, Man, and Cybernetics 42(4), 1064–1071 (2012)
15.
Konrad, P.: The abc of EMG. A practical introduction to kinesiological electromyography (April), 1–60 (2005)
16.
Lee, S., Sankai, Y.: Power assist control for walking aid with HAL-3 based on EMG and impedance adjustment around knee joint. In: IEEE/RSJ International Conference on Intelligent Robots and System. vol. 2, pp. 1499–1504 (2002)
17.
Lenzi, T., De Rossi, S., Vitiello, N., Carrozza, M.: Intention-based EMG control for powered exoskeletons. IEEE Transactions on Biomedical Engineering 59(8), 2180–2190 (2012)
18.
Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. pp. 9–14. IEEE (2010)
19.
Lloyd, D.G., Besier, T.F.: An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. Journal of Biomechanics 36(6), 765–776 (2003)
20.
Manal, K., Gravare-Silbernagel, K., Buchanan, T.S.: A Real-time EMG-driven Musculoskeletal Model of the Ankle. Multibody System Dynamics 28(1–2), 169–180 (2012)
21.
Michieletto, S., Chessa, N., Menegatti, M.: Learning how to approach industrial robot tasks from natural demonstrations. In: IEEE Workshop on Advanced Robotics and its Social Impacts (2013)
22.
Millán, J.d.R., Ferrez, P.W., Galán, F., Lew, E., Chavarriaga, R.: Non-invasive brain-machine interaction. International Journal of Pattern Recognition and Artificial Intelligence 22(05), 959–972 (2008)
23.
Millán, J.d.R., Renkens, F., Mouriño, J., Gerstner, W.: Brain-actuated interaction. Artificial Intelligence 159(1–2), 241–259 (2004)
24.
Phinyomark, A., Limsakul, C., Phukpattaranont, P.: Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification. Measurement Science Review 11(2), 45–52 (2011)
25.
Reynolds, D.A., Rose, R.C.: Robust text-independent speaker identification using gaussian mixture speaker models. Speech and Audio Processing, IEEE Transactions on 3(1), 72–83 (1995)
26.
Romero, J., Feix, T., Kjellstrom, H., Kragic, D.: Spatio-temporal modeling of grasping actions. In: IEEE/RSJ International Conference on Intelligent Robots and System. pp. 2103–2108 (2010)
27.
Sartori, M., Reggiani, M., Farina, D., Lloyd, D.G.: EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity. PloS one 7(12), e52618 (2012)
28.
Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6(2), 461–464 (1978)
29.
Suresh, M.: GMM modeling of person information from EMG signals. IEEE Recent Advances in Intelligent Computational Systems pp. 712–717 (2011)
30.
Vanacker, G., Millán, J.d.R., Lew, E., Ferrez, P.W., Galán, F., Philips, J., van Brussel, H., Nuttin, M.: Context-based filtering for assisted brain-actuated wheelchair driving. Computational Intelligence and Neuroscience 2007, 1–12 (2007)
31.
Wallace, C.S., Dowe, D.L.: Minimum message length and kolmogorov complexity. The Computer Journal 42(4), 270–283 (1999)
32.
Wang, L., Buchanan, T.S.: Prediction of joint moments using a neural network model of muscle activations from EMG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering 10, 30–37 (2002)
Metadaten
Titel
GMM-Based Single-Joint Angle Estimation Using EMG Signals
verfasst von
Stefano Michieletto
Luca Tonin
Mauro Antonello
Roberto Bortoletto
Fabiola Spolaor
Enrico Pagello
Emanuele Menegatti
Copyright-Jahr
2016
Buch
Intelligent Autonomous Systems 13

Print ISBN: 978-3-319-08337-7

Electronic ISBN: 978-3-319-08338-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-08338-4_85

Premium Partner

    Bildnachweise