Review of control strategies for robotic movement training after neurologic injury

Marchal–Crespo, Laura; Reinkensmeyer, David J.

doi:10.1186/1743-0003-6-20

Cited by 969 publications

(732 citation statements)

References 158 publications

(279 reference statements)

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“…Exoskeletons appeared to be ideal technical devices for this strategy implementation. Different types of exoskeletons have already been actively used in the clinic and the number of these developments has been growing exponentially in the last 10 years [7].…”

mentioning

confidence: 99%

Exoskeleton Control System Based on Motor-Imaginary Brain–Computer Interface

Gordleeva¹,

Lukoyanov²,

Mineev³

et al. 2017

Sovrem Tehnol Med

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The aim of the investigation was to develop the neuro-integrated control system for a lower-limb robotic exoskeleton (RE) using brain-computer interface (BCI) technology based on recognition of EEG patterns evoked by motor imagery of limb movement.Materials and Methods. The proposed neuro-integrated RE control system based on BCI technology consists of three main modules: EEG signal recording module, EEG signal classifier and the software for transmission of commands to RE. EEG patterns evoked by motor imagery are recognized by the classifier based on linear discriminant analysis that uses the features identified by spatial filtering applying CSP method for all types of commands pairwise. The proposed algorithms for classification of motor imagery patterns and user training techniques make it possible to reliably distinguish several (up to 4) different commands. After training and testing the classifier, the operator may proceed to control the external device, i.e. the lower-limb RE. RE control software has been developed for easy system customization. The software has a simple graphical user interface and allows the user to change the mapping of RE patterns and commands in the operation process.Results. As a result of testing in 14 healthy volunteers, the average accuracy of lower limb exoskeleton control based on the developed motor imagery BCI for three commands was found to average 70% in three sessions.Conclusion. The developed RE control system based on BCI technology offers fairly high accuracy for three commands. The operators successfully learn to practice motor imagery and operate the BCI contour, even if they have no previous experience of work with brainmachine interfaces.

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mentioning

confidence: 99%

Exoskeleton Control System Based on Motor-Imaginary Brain–Computer Interface

Gordleeva¹,

Lukoyanov²,

Mineev³

et al. 2017

Sovrem Tehnol Med

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“…The robot joint torque will be controlled so that the control torque will not exceed the maximum torque set by the torque limit. This controller make the robot more safety when compare to the rehabilitation robot which using only motion control strategy [13]. Moreover, the measurement of load torque , used in our control strategy, is measured by using the current measurements of the motor drivers.…”

Section: Control Strategy Of Each Jointmentioning

confidence: 99%

“…For the control strategies using in the rehabilitation robot, several control strategies have been proposed and studied [13]. There are two categories of control strategies that suit for the active device: assistive control strategy and challenge-based control strategy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Design and Implementation of a 4-DOF Upper Limb Exoskeleton for Stroke Rehabilitation with Active Assistive Control Strategy

Sutapun¹,

Sangveraphunsiri²

2017

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Abstract. We developed a robot, CUREs (Chulalongkorn University Rehabilitation Robotic Exoskeleton system), for upper extremity rehabilitation. The active assistive control strategy based on the impedance force control is developed and implemented to obtain assistive-resistive paths tracking for rehabilitation activities. The desired trajectory or rehabilitated training pattern for each specific patient need to be assigned first by a medical doctor and a physical therapy. The therapist can program the desired trajectory by guiding the patient arm based on the assigned path pattern and the set of via points will be stored and used for generating the desired trajectory. The desired trajectory will be stored specific for the patient and can be called back anytime. During the rehabilitation, the robot can assist and resist the patient's arm to follow the desired trajectory. If the patient has difficulty moving his arm to track the desired path, the robot will help by adding more torque to help the patient to move his arm to reduce the error between the desired path and the actual posture. And if the patient himself can move his arm tracking the desired path, the robot will not apply any more force to assist or resist. The necessary state variables such as angular position and torque can be recorded during the training. The main purpose of the experiment, follow the medical ethic, is to assure that there is no side effect for using this rehabilitation robot. Five subacute stroke patients participated in this pilot study. All patients have severe upper extremity weakness. The medical doctor will assign the training pattern based on patient condition. The result showed that the Fugl-Meyer Assessment Upper Extremity Scale was improved after 10 days of training in all participants without any sign of side effect.

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“…During robot-assisted therapy, the robot supports the patient's limb; the therapist is usually not in direct contact and, therefore, less able to assess effort. Many robots automatically adapt their support to the needs of the patient with a patient-cooperative control strategy [4][5]. The goal of these controllers is to assist as needed and ensure that the patient can perform the demanded movement.…”

Section: Introductionmentioning

confidence: 99%

Estimating the patient�s contribution during robot-assisted therapy

Guidali¹,

Keller²,

Klamroth-Marganska³

et al. 2013

JRRD

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Abstract-Robot-assisted therapy has become increasingly common in neurorehabilitation. Sophisticated controllers have been developed for robots to assist and cooperate with the patient. It is difficult for the patient to judge to what extent the robot contributes to the execution of a movement. Therefore, methods to comprehensively quantify the patient's contribution and provide feedback are of key importance. We developed a method comprehensively to estimate the patient's contribution by combining kinematic measures and the motor assistance applied. Inverse dynamic models of the robot and the passive human arm calculate the required torques to move the robot and the arm and build, together with the recorded motor torque, a metric (in percentage) that represents the patient's contribution to the movement. To evaluate the developed metric, 12 nondisabled subjects and 7 patients with neurological problems simulated instructed movement contributions. The results are compared with a common performance metric. The estimation shows very satisfying results for both groups, even though the arm model used was strongly simplified. Displaying this metric to patients during therapy can potentially motivate them to actively participate in the training.

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Review of control strategies for robotic movement training after neurologic injury

Cited by 969 publications

References 158 publications

Exoskeleton Control System Based on Motor-Imaginary Brain–Computer Interface

Exoskeleton Control System Based on Motor-Imaginary Brain–Computer Interface

A Novel Design and Implementation of a 4-DOF Upper Limb Exoskeleton for Stroke Rehabilitation with Active Assistive Control Strategy

Estimating the patient�s contribution during robot-assisted therapy

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