Human-Robot Interaction Torque Estimation Methods for a Lower Limb Rehabilitation Robotic System with Uncertainties

Juan C. Yepes, Santiago Rúa, Marisol Osorio, Vera Z. Pérez, Jaime A. Moreno, Adel Al-Jumaily, Manuel J. Betancur

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    Abstract

    Lower limb rehabilitation robot (LLRR) users, to successfully conduct isotonic exercises, require real-time feedback on the torque they exert on the robot to meet the goal of the treatment. Still, direct torque measuring is expensive, and indirect encoder-based estimation strategies, such as inverse dynamics (ID) and Nonlinear Disturbance Observers (NDO), are sensitive to Body Segment Inertial Parameters (BSIPs) uncertainties. We envision a way to minimize such parametric uncertainties. This paper proposes two human–robot interaction torque estimation methods: the Identified ID-based method (IID) and the Identified NDO-based method (INDO). Evaluating in simulation the proposal to apply, in each rehabilitation session, a sequential two-phase method: (1) An initial calibration phase will use an online parameter estimation to reduce sensitivity to BSIPs uncertainties. (2) The torque estimation phase uses the estimated parameters to obtain a better result. We conducted simulations under signal-to-noise ratio (SNR) = 40 dB and 20% BSIPs uncertainties. In addition, we compared the effectiveness with two of the best methods reported in the literature via simulation. Both proposed methods obtained the best Coefficient of Correlation, Mean Absolute Error, and Root Mean Squared Error compared to the benchmarks. Moreover, the IID and INDO fulfilled more than 72.2% and 88.9% of the requirements, respectively. In contrast, both methods reported in the literature only accomplish 27.8% and 33.3% of the requirements when using simulations under noise and BSIPs uncertainties. Therefore, this paper extends two methods reported in the literature and copes with BSIPs uncertainties without using additional sensors.

    Original languageEnglish
    Article number5529
    JournalApplied Sciences (Switzerland)
    Volume12
    Issue number11
    DOIs
    StatePublished - 1 Jun 2022

    Bibliographical note

    Funding Information:
    Funding: This paper was framed under the project “KINA: Virtual Reality System for Lower Limb Rehabilitation of APM or IED Victims” funded by the call 808 of 2018 for projects from the Ministerio de Ciencia, Tecnologia e Innovacion de Colombia (Minciencias), project 121080864383. Moreover, the Ph.D. studies of Juan C. Yepes were funded by Minciencias and the Universidad Pontificia Bolivariana (UPB) under call 727 of 2015. In addition, the Australian Academy of Science (AAS) and Minciencias granted the funds for his Ph.D. internship within the Australia-Americas Ph.D. Research Internships Program 2019. Additionally, this work was supported by UNAM-PAPIIT IN 102121. Finally, this work was supported by Universidad de Medellín (UdeM).

    Funding Information:
    This paper was framed under the project “KINA: Virtual Reality System for Lower Limb Rehabilitation of APM or IED Victims” funded by the call 808 of 2018 for projects from the Ministerio de Ciencia, Tecnologia e Innovacion de Colombia (Minciencias), project 121080864383. Moreover, the Ph.D. studies of Juan C. Yepes were funded by Minciencias and the Universidad Pontificia Bolivariana (UPB) under call 727 of 2015. In addition, the Australian Academy of Science (AAS) and Minciencias granted the funds for his Ph.D. internship within the Australia-Americas Ph.D. Research Internships Program 2019. Additionally, this work was supported by UNAM-PAPIIT IN 102121. Finally, this work was supported by Universidad de Medellín (UdeM).

    Publisher Copyright:
    © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

    Keywords

    • computer simulation
    • exoskeletons
    • force feedback
    • mathematical model
    • nonlinear systems
    • system identification

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