• DocumentCode
    3660389
  • Title

    Teleoperated robot writing using EMG signals

  • Author

    Chenguang Yang;Sai Chang;Peidong Liang;Zhijun Li;Chun-Yi Su

  • Author_Institution
    Center for Robotics and Neural Systems, Plymouth University PL4 8AA, UK
  • fYear
    2015
  • Firstpage
    2264
  • Lastpage
    2269
  • Abstract
    In this paper, we have developed a method to tele-control a robot arm to imitate human writing skills using electromyography (EMG) signals. The control method is implemented on the Baxter® robot with a brush attached on the endpoint of its arm to imitate human writing skills, and a MYO sensor is used to measure the surface electromyographic (sEMG) signals generated by contractions of human muscles involved in writing tasks. A haptic device Sensable® Omni is used to control the motion of the Baxter® robot arm, and V-Rep® is employed to simulate the movement of the arm and to calculate the inverse kinematic of Baxter® robot arm. All the communications for Baxter® robot, V-Rep simulator, Omni device and MYO are integrated in MATLAB®/Simulink®. The main test is to make the Baxter® arm following the movement of a human subject when writing with Omni stylus, and the EMG signals are processed using low pass filter and moving average technique to extract the smoothed envelope which is utilised to control the variation of position instead of variation of force along the vertical Z axis, so when the operator writes with force variations, the Baxter® can draw lines with variable thickness. This imitation system is successfully tested to make the Baxter® arm to follow the writing movement of the operator´s hand and would have potential applications in more complicated human robot teleoperation tasks such as tele-rehabilitation and tele-surgery.
  • Keywords
    "Electromyography","Robot sensing systems","Joints","Phantoms","Muscles","Writing"
  • Publisher
    ieee
  • Conference_Titel
    Information and Automation, 2015 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/ICInfA.2015.7279663
  • Filename
    7279663