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Online Simulation of Mechatronic Neural Interface Systems: Two Case-Studies

  • Samuel BustamanteEmail author
  • Juan C. Yepes
  • Vera Z. Pérez
  • Julio C. Correa
  • Manuel J. Betancur
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 690)

Abstract

Neural interface systems (NIS) are widely used in rehabilitation and prosthetics. These systems usually involve robots, such as robotic exoskeletons or mechatronic arms, as terminal devices. We propose a methodology to assess the feasibility of implementing these kind of neural interfaces by means of an online kinematic simulation of the robot. It allows the researcher or developer to make tests and improve the design of the mechatronic devices when they have not been built yet or are not available. Moreover, it may be used in biofeedback applications for rehabilitation. The simulation makes use of the CAD model of the robot, its Denavit-Hartenberg parameters, and biosignals recorded from a human being. The proposed methodology was tested using surface electromyography (sEMG) signals from the upper limb of a 25-year-old subject to control a kinematic simulation of a KUKA KR6 robot.

It was also used in the design process of an actual lower limb rehabilitation system being developed in our laboratories. The 3D computational simulation of this robot was successfully controlled by means of sEMG signals acquired from the lower limb of a 26-year-old healthy subject. Both real-time and prerecorded signals were used. The tests provided researchers feedback in the design process, looking forward to new iterations in the detailed design and construction phases of the project.

Keywords

Root Mean Square Kinematic Model Biceps Brachii Rectus Femoris sEMG Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors would like to thank Cristian D. Martínez for the design and development of the Low-Cost sEMG signal acquisition device. We also would like to thank the physiotherapist Vanessa Montoya for her advisory with the rehabilitation exercises, Álvaro J. Saldarriaga for his support simulating the real-time sEMG signals, and Andrs Orozco-Duque for his support in the acquisition of sEMG signals.

Finally, the authors express gratitude to the Departamento Administrativo de Ciencia, Tecnología e InnovaciÓn Colciencias, from Colombia, for their grant number 121071149736.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Samuel Bustamante
    • 1
    Email author
  • Juan C. Yepes
    • 2
  • Vera Z. Pérez
    • 3
  • Julio C. Correa
    • 4
  • Manuel J. Betancur
    • 5
  1. 1.Grupo de Automática y Diseńo A+DUniversidad Pontificia BolivarianaMedellínColombia
  2. 2.Grupo de Investigaciones en Bioingeniería, Grupo de Automática y Diseío A+DUniversidad Pontificia BolivarianaMedellínColombia
  3. 3.Facultad de Ingeniería Eléctrica y Electrónica, Grupo de Investigaciones en BioingenieríaUniversidad Pontificia BolivarianaMedellínColombia
  4. 4.Facultad de Ingeniería Mecánica, Grupo de Automática y Diseńo A+DUniversidad Pontificia BolivarianaMedellínColombia
  5. 5.Facultad de Ingeniería Eléctrica y Electrónica, Grupo de Automática y Diseńo A+DUniversidad Pontificia BolivarianaMedellínColombia

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