Estimation of elbow joint angle by NARX model using EMG data

Moosa Ayati, Armin Ehrampoosh, Aghil Yousefi-Koma

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

6 Citations (Scopus)


Myoelectric control has a key role in human-machine interface applications such as orthosis control and teleoperation. Myoelectric signals are bio signals that are detectable from surface of the skin, and contain useful information about user's moving intention. This paper presents a methodology to estimate elbow joint angle from muscle's data using neural network (NN). Proposed methodology can be expanded to estimate any joint angle by recording muscle activities concerning with the joint. In addition, Nonlinear Auto-Regressive eXogenous-NN (NARX-NN) model is selected to estimate joint angle. Several data sets are recorded and processed to train and test the NN. The trained network is used to predict elbow angles for individual data sets. Results show that trained network can estimate joint angle with an acceptable performance.

Original languageEnglish
Title of host publicationInternational Conference on Robotics and Mechatronics, ICROM 2015
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781467372343
Publication statusPublished - 28 Dec 2015
Externally publishedYes
EventRSI/ISM International Conference on Robotics and Mechatronics 2015 - Tehran, Iran
Duration: 7 Oct 20159 Oct 2015
Conference number: 3rd (Proceedings)


ConferenceRSI/ISM International Conference on Robotics and Mechatronics 2015
Abbreviated titleICROM 2015
Internet address


  • EMG
  • Myoelectric control
  • NARX
  • Neural network
  • Rehabilitation

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