A Real-Time Classification System for Upper Limb Prosthesis Control in MATLAB

  • Andreas AttenbergerEmail author
  • Sławomir Wojciechowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10672)


In this paper we present a MATLAB tool for processing both EMG and NIR sensor signals in real-time in order to provide a fully operational tool for clinical testing. After a short training phase, the decision tree classifier produces output for actuating a Michelangelo hand by Ottobock Healthcare. To validate the system design, it was tested with four probands performing wrist flexion, wrist extension and fist hand movement patterns. After a training phase, features were extracted in real-time from either the EMG or NIR sensor data for classification with the model created during the training phase. In this setup, NIR sensor data alone proved to be sufficient for distinguishing three hand movement patterns with two sensors. The classification accuracy is equal or better to standard EMG data recorded from the same sensor pick-up area on the forearm.


Prosthesis control Machine learning EMG signal NIR signal 



The authors are grateful to Prof. Klaus Buchenrieder of the Universität der Bundeswehr for his support of their research as well as Otto Bock Healthcare for supplying the Michelangelo hand employed for testing the control scheme.


  1. 1.
    Jiang, N., Dosen, S., Müller, K.R., Farina, D.: Myoelectric control of artificial limbs - is there a need to change focus. IEEE Signal Process. Mag. 29(5), 148–152 (2012)Google Scholar
  2. 2.
    Peerdeman, B., Boere, D., Witteveen, H.J.B., Huis in ’t Veld, M.H.A., Hermens, H.J., Stramigioli, S., Rietman, J.S., Veltink, P.H., Misra, S.: Myoelectric forearm prostheses: state of the art from a user-centered perspective. J. Rehabil. Res. Develop. 48(6), 719–738 (2011)CrossRefGoogle Scholar
  3. 3.
    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. Develop. 48(6), 643 (2011)CrossRefGoogle Scholar
  4. 4.
    Attenberger, A., Buchenrieder, K.: Modeling and visualization of classification-based control schemes for upper limb prostheses. In: Popovic, M., Schätz, B., Voss, S., (eds.) IEEE 19th International Conference and Workshops on Engineering of Computer-Based Systems, ECBS 2012, Novi Sad, Serbia, 11–13 Apr 2012, pp. 188–194. IEEE Computer Society (2012)Google Scholar
  5. 5.
    Englehart, K., Hudgins, B., Parker, P., Stevenson, M.: Classification of the myoelectric signal using time-frequency based representations. Med. Eng. Phys. 21(6–7), 431–438 (1999)CrossRefGoogle Scholar
  6. 6.
    Herrmann, S.: Direkte und proportionale Ansteuerung einzelner Finger von Handprothesen. Verlag Dr. Hut, Munich (2011)Google Scholar
  7. 7.
    Buchenrieder, K.: Dimensionality reduction for the control of powered upper limb prostheses. In: Proceedings of the 14th Annual IEEE International Conference and Workshops on the Engineering of Computer-Based Systems (ECBS 2007), pp. 327–333, March 2007Google Scholar
  8. 8.
    Attenberger, A.: Time analysis for improved upper limb movement classification: Dissertation. Universität der Bundeswehr München, Neubiberg (2016)Google Scholar
  9. 9.
    Herrmann, S., Attenberger, A., Buchenrieder, K.: Prostheses control with combined near-infrared and myoelectric signals. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2011. LNCS, vol. 6928, pp. 601–608. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  10. 10.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press, Cambridge (2008)zbMATHGoogle Scholar
  11. 11.
    Wojciechoswki, S.: A MATLAB classification system for upper limb prosthesis control. Master’s thesis, Wrocław University of Science and Technology, Wrocław, Poland (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.FH KufsteinUniversity of Applied SciencesKufsteinAustria
  2. 2.Wrocław University of Science and TechnologyWrocławPoland

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