Learning task-specific models for reach to grasp movements: Towards EMG-based teleoperation of robotic arm-hand systems

Minas V. Liarokapis, Panagiotis Artemiadis, Pantelis T. Katsiaris, Kostas J. Kyriakopoulos

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

10 Citations (Scopus)

Abstract

A learning scheme based on Random Forests is used to decode the EMG activity of 16 muscles of the human arm-hand system to a continuous representation of kinematics in reach-to-grasp movements in 3D space. Classification methods are used to discriminate between significantly different reach to grasp strategies, formulating a switching mechanism that may trigger the use of position and object-specific decoding models (task-specificity). These task-specific models can achieve better estimation results than the general models for the kinematics of different reach-to-grasp movements. The efficacy of the proposed methodology is assessed through a strict validation procedure, based on everyday life reach-to-grasp scenarios and data not previously seen during training. Finally, for demonstration purposes, the authors teleoperate an arm-hand model in the OpenRave simulation environment using the estimated from the EMG signals human motion.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
Pages1287-1292
Number of pages6
DOIs
StatePublished - 2012
Event2012 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2012 - Rome, Italy
Duration: Jun 24 2012Jun 27 2012

Other

Other2012 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2012
CountryItaly
CityRome
Period6/24/126/27/12

Fingerprint

Robotic arms
End effectors
Remote control
Kinematics
Decoding
Muscle
Demonstrations

Keywords

  • ElectroMyoGraphy (EMG)
  • EMG-Based Tele-operation
  • Learning Scheme
  • Model Switching
  • Random Forests
  • Robotic Arm-Hand System

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biomedical Engineering
  • Mechanical Engineering

Cite this

Liarokapis, M. V., Artemiadis, P., Katsiaris, P. T., & Kyriakopoulos, K. J. (2012). Learning task-specific models for reach to grasp movements: Towards EMG-based teleoperation of robotic arm-hand systems. In Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (pp. 1287-1292). [6290724] https://doi.org/10.1109/BioRob.2012.6290724

Learning task-specific models for reach to grasp movements : Towards EMG-based teleoperation of robotic arm-hand systems. / Liarokapis, Minas V.; Artemiadis, Panagiotis; Katsiaris, Pantelis T.; Kyriakopoulos, Kostas J.

Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics. 2012. p. 1287-1292 6290724.

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

Liarokapis, MV, Artemiadis, P, Katsiaris, PT & Kyriakopoulos, KJ 2012, Learning task-specific models for reach to grasp movements: Towards EMG-based teleoperation of robotic arm-hand systems. in Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics., 6290724, pp. 1287-1292, 2012 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2012, Rome, Italy, 6/24/12. https://doi.org/10.1109/BioRob.2012.6290724
Liarokapis MV, Artemiadis P, Katsiaris PT, Kyriakopoulos KJ. Learning task-specific models for reach to grasp movements: Towards EMG-based teleoperation of robotic arm-hand systems. In Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics. 2012. p. 1287-1292. 6290724 https://doi.org/10.1109/BioRob.2012.6290724
Liarokapis, Minas V. ; Artemiadis, Panagiotis ; Katsiaris, Pantelis T. ; Kyriakopoulos, Kostas J. / Learning task-specific models for reach to grasp movements : Towards EMG-based teleoperation of robotic arm-hand systems. Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics. 2012. pp. 1287-1292
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