A Comparison of Supervised Learning Algorithms for Telerobotic Control Using Electromyography Signals

Authors

  • Tyler Frasca Wentworth Institute of Technology
  • Antonio Sestito Wentworth Institute of Technology
  • Craig Versek NeuroFieldz and Northeastern University
  • Douglas Dow Wentworth Institute of Technology
  • Barry Husowitz Wentworth Institute of Technology
  • Nate Derbinsky Wentworth Institute of Technology

DOI:

https://doi.org/10.1609/aaai.v30i1.9926

Keywords:

Robotics, Machine Learning, Human-Robot Interaction

Abstract

Human Computer Interaction (HCI) is central for many applications, including hazardous environment inspection and telemedicine. Whereas traditional methods ofHCI for teleoperating electromechanical systems include joysticks, levers, or buttons, our research focuses on using electromyography (EMG) signals to improve intuition and response time. An important challenge is to accurately and efficiently extract and map EMG signals to known position for real-time control. In this preliminary work, we compare the accuracy and real-time performance of several machine-learning techniques for recognizing specific arm positions. We present results from offline analysis, as well as end-to-end operation using a robotic arm.

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Published

2016-03-05

How to Cite

Frasca, T., Sestito, A., Versek, C., Dow, D., Husowitz, B., & Derbinsky, N. (2016). A Comparison of Supervised Learning Algorithms for Telerobotic Control Using Electromyography Signals. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9926