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Research Papers

Patient-Robot-Therapist Collaboration Using Resistive Impedance Controlled Tele-Robotic Systems Subjected to Time Delays

[+] Author and Article Information
Mojtaba Sharifi

Department of Mechanical Engineering,
Sharif University of Technology,
Azadi Street,
Tehran 11155-9567, Iran;
Department of Electrical and
Computer Engineering,
University of Alberta,
Edmonton, AB T6G 1H9, Canada
e-mail: sharifi3@ualberta.ca

Hassan Salarieh

Department of Mechanical Engineering,
Sharif University of Technology,
Azadi Street,
Tehran 11155-9567, Iran
e-mail: salarieh@sharif.edu

Saeed Behzadipour

Department of Mechanical Engineering,
Sharif University of Technology,
Azadi Street,
Tehran 11155-9567, Iran
e-mail: behzadipour@sharif.edu

Mahdi Tavakoli

Department of Electrical and
Computer Engineering,
University of Alberta,
Edmonton, AB T6G 1H9, Canada
e-mail: mahdi.tavakoli@ualberta.ca

1Corresponding author.

Contributed by the Mechanisms and Robotics Committee of ASME for publication in the JOURNAL OF MECHANISMS AND ROBOTICS. Manuscript received July 13, 2017; final manuscript received July 15, 2018; published online August 27, 2018. Assoc. Editor: Marcia K. O'Malley.

J. Mechanisms Robotics 10(6), 061003 (Aug 27, 2018) (17 pages) Paper No: JMR-17-1209; doi: 10.1115/1.4040961 History: Received July 13, 2017; Revised July 15, 2018

In this paper, an approach to physical collaboration between a patient and a therapist is proposed using a bilateral impedance control strategy developed for delayed tele-robotic systems. The patient performs a tele-rehabilitation task in a resistive virtual environment with the help of online assistive forces from the therapist being provided through teleoperation. Using this strategy, the patient's involuntary hand tremors can be filtered out and the effort of severely impaired patients can be amplified in order to facilitate their early engagement in physical tasks. The response of the first desired impedance model is tracked by the master robot (interacting with the patient), and the master trajectory plus a deviation as the response of the second impedance model is tracked by the slave robot (interacting with the therapist). Note that the first impedance model is a virtual mass-damper-spring system that has a response trajectory to the combination of patient and therapist forces. Similarly, the second impedance model is a virtual mass-damper-spring system that generates the desired slave–master deviation trajectory as its response to the therapist force. Transmitted signals through the communication channels are subjected to time delays, which exist in home-based rehabilitation (i.e., tele-rehabilitation). Tracking of the impedance models responses in the presence of modeling uncertainties is achieved by employing a nonlinear bilateral adaptive controller and proven using a Lyapunov analysis. The stability of delayed teleoperation system is also proven using the absolute stability criterion. The proposed control method is experimentally evaluated for patient–therapist collaboration in resistive/assistive tasks. In these experiments, a healthy human operator simulates a poststroke patient behavior during the interaction with the master robot.

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Figures

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Fig. 1

The proposed resistive/assistive tele-rehabilitation strategy using an impedance-controlled tele-robotic system

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Fig. 2

The magnitude Bode diagram of an under-damped linear second order system (with natural frequency of ωndes). The oscillatory tremor is assumed to have a minimum frequency of ωtremmin.

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Fig. 3

Block diagram of the nonlinear bilateral adaptive control of master and slave robots for resistive/assistive tele-rehabilitation

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Fig. 4

Experimental tele-robotic system for resistive/assistive tele-rehabilitation: (a) Phantom premium robot (slave), and (b) Quanser Rehab robot (master), where healthy human operators behave as the therapist and patient

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Fig. 5

The scaled interaction forces (μffth and fpa) in (a) x and (b) y directions for the resistive/assistive tele-rehabilitation of a patient with moderate disability and hand tremors (simulated by a healthy operator)

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Fig. 6

The patient (master) and the therapist (slave) positions with the desired impedance response in (a) x and (b) y directions of resistive environment

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Fig. 7

The master and slave position tracking errors with respect to impedance responses (x̃m and x̃s) and the slave deviation from the master position (xs−μpxmd) in (a) x and (b) y directions

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Fig. 8

Trajectories of the patient, the therapist, and the desired impedance models responses in the x−y plane during the resistive/assistive tele-rehabilitation

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Fig. 9

The positions of the patient's and the therapist's hands and the master and slave impedance models responses in (a) x and (b) y directions during a tele-rehabilitation task for a severely impaired patient (simulated by a healthy operator)

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Fig. 10

The patient and the scaled therapist forces (fpa and μffth) in (a) x and (b) y directions for a severely impaired patient having considerable hand tremors (simulated by a healthy operator)

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Fig. 11

The master and slave position tracking errors with respect to impedance responses (x̃m and x̃s) and the slave deviation from the master position (xs−μpxmd) in (a) x and (b) y directions

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Fig. 12

The scaled interaction forces (μffth and fpa), (b) the patient (master) and the therapist (slave) positions and their desired impedance responses, and (c) the master and slave position tracking errors (x̃m and x̃s) and the slave deviation from the master position (xdess−μpxm d), in y direction, for a patient with moderate disability and hand tremors (simulated by a healthy operator)

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Fig. 13

Trajectories of the patient, the therapist, and the desired impedance models responses in the x−y plane during the resistive/assistive tele-rehabilitation

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