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Trajectory Adaptation and Learning for Ankle Rehabilitation Using a 3-PRS Parallel Robot

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Intelligent Robotics and Applications (ICIRA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9245))

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Abstract

This paper presents a methodology for learning and adaptation of a 3-PRS parallel robot skills for ankle rehabilitation. Passive exercises have been designed to train dorsi/plantar flexion, inversion/eversion ankle movements. During exercises, forces may be high because patient cannot follow the desired trajectory. While small errors in the desired trajectory can cause important deviations in the desired forces, pure position control is inappropriate for tasks that require physical contact with the environment. The proposed algorithm takes as input the reference trajectory and force profile, then adapts the robot movement by introducing small offsets to the reference trajectory so that the resulting forces exerted by the patient match the reference profile. The learning procedure is based on Dynamic Movement Primitives (DMPs).

F.J. Abu-Dakk—This work was partially financed by Plan Nacional de I+D, Comisión Interministerial de Ciencia y Tecnologa (FEDER-CICYT) under the project DPI2013-44227-R.-This research was also partially funded by the MINISTERIO DE ECONOMÍA Y COMPETITIVIDAD under the project RTC-2014-3070-5

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Author information

Authors and Affiliations

  1. Robotics Lab, Carlos III University of Madrid, Av. Universidad, 30, 28911, Leganés (Madrid), Spain

    Fares J. Abu-Dakk, J.A. Escalera & M. Abderrahim

  2. Departamento de Ingeniería de Sistemas y Automática, Universidad Politécnica de Valencia, 46022, Valencia, Spain

    A. Valera, M. Vallés & V. Mata

Authors
  1. Fares J. Abu-Dakk
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  2. A. Valera
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  3. J.A. Escalera
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  4. M. Vallés
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  5. V. Mata
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  6. M. Abderrahim
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Correspondence to Fares J. Abu-Dakk .

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Editors and Affiliations

  1. University of Portsmouth, Portsmouth, United Kingdom

    Honghai Liu

  2. Tokyo Metropolitan University, Tokyo, Japan

    Naoyuki Kubota

  3. Shanghai Jiao Tong University, Shanghai, China

    Xiangyang Zhu

  4. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Rüdiger Dillmann

  5. University of Portsmouth, Portsmouth, United Kingdom

    Dalin Zhou

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Abu-Dakk, F.J., Valera, A., Escalera, J., Vallés, M., Mata, V., Abderrahim, M. (2015). Trajectory Adaptation and Learning for Ankle Rehabilitation Using a 3-PRS Parallel Robot. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2015. Lecture Notes in Computer Science(), vol 9245. Springer, Cham. https://doi.org/10.1007/978-3-319-22876-1_41

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  • DOI: https://doi.org/10.1007/978-3-319-22876-1_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22875-4

  • Online ISBN: 978-3-319-22876-1

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