Human impedance parameter estimation using artificial neural network for modelling physiotherapist motion

Demir, Uğur; Kocaoğlu, Sıtkı; Akdoğan, Erhan

doi:10.1016/j.bbe.2016.01.002

Cited by 13 publications

(9 citation statements)

References 18 publications

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“…3,4 Many rehabilitation robots have been devised in recent years. [5][6][7][8][9][10][11][12][13][14][15][16] Mehmet Arif Adli proposed a therapeutic exercise robot for knee and hip rehabilitation. 5 This robot owned flexion-extension degree of freedom in knee joint, flexion-extension and abduction-adduction degrees of freedom in hip joint.…”

Section: Introductionmentioning

confidence: 99%

“…Demir U employed a limb rehabilitation robot system to study impedance parameter estimation method using ANN. 6 Mohan et al devised a vertical planar 2PRP-2PPR robot for lower limb rehabilitation. 7 In their research, system dynamics was derived and a robust controlling scheme using non-singular fast terminal sliding mode control along with a nonlinear disturbance observer was proposed for the robot.…”

Section: Introductionmentioning

confidence: 99%

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An Rehabilitation Robot Driven by Pneumatic Artificial Muscles

Zhong

Zhao

et al. 2020

J. Mech. Med. Biol.

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Rehabilitation robots are playing an important role in restoring movement ability of hemiplegic patients. However, most of these robots adopt motors as actuators. Considering human body is a flexible organism, rigid motors lack compliance when getting in touch with patients. This paper designs an ankle rehabilitation robot by employing pneumatic muscle actuators which are soft and have similar compliance with biological muscles. Analysis of motion characteristics of human ankle is performed, and relationship between angle and torque of human ankle acquired from experiment is studied. Driving mechanism using pneumatic muscle actuators is addressed carefully and ankle-rehabilitation robot is designed. Then, dynamics of the robot is established and structure optimization of the driving mechanism is performed. Consequently, prototype is manufactured and assembled.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Rehabilitation Robot Driven by Pneumatic Artificial Muscles

Zhong

Zhao

et al. 2020

J. Mech. Med. Biol.

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“…Apart from using only kinematic data, dynamics data such as joint forces can also be used for the neural network control. U. Demir et al used the neural network with inputs of the angle and force of the Physiotherabot to estimate impedance parameters during manual therapy, which can make the Physiotherabot suitable for the personal robotic treatment of different patients [24]. J. Jung et al used the ground reaction force and joint angles of the sound leg as inputs of the neural network, which can recognize current phase gait and generate predicted motions of the unsound leg [25].…”

Section: Introductionmentioning

confidence: 99%

Design and Speed-Adaptive Control of a Powered Geared Five-Bar Prosthetic Knee Using BP Neural Network Gait Recognition

Sun

Huang

Jia

et al. 2019

Sensors

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To improve the multi-speed adaptability of the powered prosthetic knee, this paper presented a speed-adaptive neural network control based on a powered geared five-bar (GFB) prosthetic knee. The GFB prosthetic knee is actuated via a cylindrical cam-based nonlinear series elastic actuator that can provide the desired actuation for level-ground walking, and its attitude measurement is realized by two inertial sensors and one load cell on the prosthetic knee. To improve the performance of the control system, the motor control and the attitude measurement of the GFB prosthetic knee are run in parallel. The BP neural network uses input data from only the GFB prosthetic knee, and is trained by natural and artificially modified various gait patterns of different able-bodied subjects. To realize the speed-adaptive control, the prosthetic knee speed and gait cycle percentage are identified by the Gaussian mixture model-based gait classifier. Specific knee motion control instructions are generated by matching the neural network predicted gait percentage with the ideal walking gait. Habitual and variable speed level-ground walking experiments are conducted via an able-bodied subject, and the experimental results show that the neural network control system can handle both self-selected walking and variable speed walking with high adaptability.

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“…Researchers are paying more and more attention to improve the adaptation of rehabilitation robots to individual difference. Many researches have reported that researchers determine the equivalent impedance parameters of human upper limb online and offline by intelligent control algorithm to increase the adaptation of the robot system and improve the participants' experience [ 16 – 19 ]. Demir et al [ 19 ] analyzed the patients' mechanical impedance parameters by neural network algorithm while training with their therapist and then used the parameters to activate the robot to imitate the interaction.…”

Section: Introductionmentioning

confidence: 99%

“…Many researches have reported that researchers determine the equivalent impedance parameters of human upper limb online and offline by intelligent control algorithm to increase the adaptation of the robot system and improve the participants' experience [ 16 – 19 ]. Demir et al [ 19 ] analyzed the patients' mechanical impedance parameters by neural network algorithm while training with their therapist and then used the parameters to activate the robot to imitate the interaction. Song et al [ 20 ] developed an adaptive motion control for a 4-DOF end-effector upper limb robot based on impedance identification and confirmed that the control strategy can realize the adaption of the system among five healthy subjects' experiment.…”

Section: Introductionmentioning

confidence: 99%

Development and Implementation of an End-Effector Upper Limb Rehabilitation Robot for Hemiplegic Patients with Line and Circle Tracking Training

Liu

et al. 2017

Journal of Healthcare Engineering

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Numerous robots have been widely used to deliver rehabilitative training for hemiplegic patients to improve their functional ability. Because of the complexity and diversity of upper limb motion, customization of training patterns is one key factor during upper limb rehabilitation training. Most of the current rehabilitation robots cannot intelligently provide adaptive training parameters, and they have not been widely used in clinical rehabilitation. This article proposes a new end-effector upper limb rehabilitation robot, which is a two-link robotic arm with two active degrees of freedom. This work investigated the kinematics and dynamics of the robot system, the control system, and the realization of different rehabilitation therapies. We also explored the influence of constraint in rehabilitation therapies on interaction force and muscle activation. The deviation of the trajectory of the end effector and the required trajectory was less than 1 mm during the tasks, which demonstrated the movement accuracy of the robot. Besides, results also demonstrated the constraint exerted by the robot provided benefits for hemiplegic patients by changing muscle activation in the way similar to the movement pattern of the healthy subjects, which indicated that the robot can improve the patient's functional ability by training the normal movement pattern.

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Human impedance parameter estimation using artificial neural network for modelling physiotherapist motion

Cited by 13 publications

References 18 publications

An Rehabilitation Robot Driven by Pneumatic Artificial Muscles

An Rehabilitation Robot Driven by Pneumatic Artificial Muscles

Design and Speed-Adaptive Control of a Powered Geared Five-Bar Prosthetic Knee Using BP Neural Network Gait Recognition

Development and Implementation of an End-Effector Upper Limb Rehabilitation Robot for Hemiplegic Patients with Line and Circle Tracking Training

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