Adaptive Impedance Control Applied to Robot-Aided Neuro-Rehabilitation of the Ankle

Pérez-Ibarra, Juan C.; Siqueira, Adriano A. G.; Silva-Couto, Marcela A.; Russo, Thiago Luiz; Krebs, Hermano Igo

doi:10.1109/lra.2018.2885165

Cited by 48 publications

(20 citation statements)

References 33 publications

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“…The impedance control approach [124][125][126] is employed when a mechatronic device is in contact with its environment or with a human. It is an important control concept in modern robotics that is demonstrated by numerous applications [127][128][129][130][131][132][133][134][135][136][137][138][139][140]. The mechanical impedance, which features a force response of a body or a system of bodies on imposed velocity or position, can be described as a force-velocity relationship or force-position relationship.…”

Section: The Impedance Controlmentioning

confidence: 99%

The Advanced Control Approach based on SMC Design for the High-Fidelity Haptic Power Lever of a Small Hybrid Electric Aircraft

Hace

2019

Energies

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In the serial hybrid electric propulsion system of a small propeller aircraft the battery state of charge is fluctuating due to the diversity of possible power flows. Overwhelming visual information on the cockpit displays, besides requiring visual pilot attention, increases pilot workload, which is undesirable, especially in risky flight situations. Haptic interfaces, on the other hand, can provide intuitive cues that can be applied to enhance and simplify the cockpit. In this paper, we deal with an enhanced power lever stick, which can provide feedback force feel with haptic cues for enhanced information flow between the pilot and the powertrain system. We present selected haptic patterns for specific information related to the fluctuating battery state of charge. The haptic patterns were designed to reduce pilot workload, and for easy use, safe and energy-efficient control of the hybrid electric powertrain system. We focus on the advanced control design for high-performance force feedback required for rendering fine haptic signals, which stimulates the sensitive haptics of a pilot’s hand-arm system. The presented control algorithm has been designed by the sliding mode control (SMC) approach in order to provide disturbance rejection and high-fidelity haptic rendering. The proposed control design has been validated on an experimental prototype system.

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Section: The Impedance Controlmentioning

confidence: 99%

The Advanced Control Approach based on SMC Design for the High-Fidelity Haptic Power Lever of a Small Hybrid Electric Aircraft

Hace

2019

Energies

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“…e Pneumatic Soft Parallel Robot [8] of the University of Auckland has no spherical joints and has the characteristics of high sensitivity, compact structure, wear resistance, easiness to carry, and light weight. Wearable ankle rehabilitation robots [9,10] are also becoming more and more popular. Multiangle movement and rotation can fully move the ankle joint, which is more conducive to the patient's return to a normal state.…”

Section: Introductionmentioning

confidence: 99%

A Novel Human-Machine Collaboration Model of an Ankle Joint Rehabilitation Robot Driven by EEG Signals

Shi

Yang

Zhang

2021

Mathematical Problems in Engineering

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With the emergence of the phenomenon of social aging, the elderly have frequent physical movement disorders. In particular, the movement disorder of the ankle joint seriously affects the daily life of the elderly. Rehabilitation robots are of great significance for improving the efficiency of rehabilitation, ensuring the quality of rehabilitation, and reducing the labor intensity of workers. As an auxiliary treatment tool, rehabilitation robots should have rich and effective motion modes. The exercise mode should be adaptable for patients with different conditions and different recovery periods. To improve the accuracy of human-computer interaction of ankle joint rehabilitation robots (AJRR), this study proposes a man-machine collaboration model of an EEG-driven AJRR. The model mainly expands from two levels (1) to establish the connection between EEG and intention so as to identify the intention. In the recognition process, first feature extraction is given on the preprocessed EEG. Convolutional neural network (CNN) is selected to extract the deep features of the EEG signal, and support vector machine (SVM) is used for classifying the deep features, thereby realizing intent recognition. (2) The result of intention recognition is input to the human-computer interaction (HCI) system, which controls the movement of the rehabilitation robot after receiving the instruction. This study truly realizes patient-oriented rehabilitation training. Experiments show that the human-machine collaboration model used can show higher accuracy of intention recognition, thereby increasing the satisfaction of using AJRR.

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“…Although this assistance strategy can allow the patient to deviate from the predetermined trajectory, the patient is limited in spatial freedom. Some studies also adopt adaptive impedance control to update the impedance parameters of the controller in real-time according to the patient's motion state [27]. Although this kind of controller could realize assistance for patients, it also restricts the spatial freedom of patients.…”

Section: Introductionmentioning

confidence: 99%

Development and Assist-As-Needed Control of an End-Effector Upper Limb Rehabilitation Robot

2020

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Robot-assisted rehabilitation therapy has been proven to effectively improve upper limb motor function and daily behavior of patients with motor dysfunction, and the demand has increased at every stage of the rehabilitation recovery. According to the motor relearning program theory, upper limb motor dysfunction can be restored by a certain amount of repetitive training. Robotics devices can be an approach to accelerate the rehabilitation process by maximizing the patients’ training intensity. This paper develops a new end-effector upper limb rehabilitation robot (EULRR) first and then presents a controller that is suitable for the assist-as-needed (AAN) training of the patients when performing the rehabilitation training. The AAN controller is a strategy that helps the patient’s arm to stay close to the given trajectory while allowing for spatial freedom. This controller enables the patient’s arm to have spatial freedom by constructing a virtual channel around the predetermined training trajectory. Patients could move their arm freely in the allowed virtual channel during rehabilitation training while the robot provides assistance when deviating from the virtual channel. The AAN controller is preliminarily tested with a healthy male subject in different conditions based on the EULRR. The experimental results demonstrate that the proposed AAN controller could provide assistance when moving out of the virtual channel and provide no assistance when moving along the trajectory within the virtual channel. In the close future, the controller is planned to be used in elderly volunteers and help to increase the intensity of the rehabilitation therapy by assisting the arm movement and by provoking active participation.

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Adaptive Impedance Control Applied to Robot-Aided Neuro-Rehabilitation of the Ankle

Cited by 48 publications

References 33 publications

The Advanced Control Approach based on SMC Design for the High-Fidelity Haptic Power Lever of a Small Hybrid Electric Aircraft

The Advanced Control Approach based on SMC Design for the High-Fidelity Haptic Power Lever of a Small Hybrid Electric Aircraft

A Novel Human-Machine Collaboration Model of an Ankle Joint Rehabilitation Robot Driven by EEG Signals

Development and Assist-As-Needed Control of an End-Effector Upper Limb Rehabilitation Robot

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