A novel approach for robot-assisted upper-limb rehabilitation

Xu, Guoqing; Gao, Xiang; Sheng, Changdong; Wang, Qiang; Zhu, Bin; Li, Jinfei

doi:10.1177/1729881417736670

Cited by 9 publications

(5 citation statements)

References 24 publications

(29 reference statements)

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“…Its human-like kinematics and high back drivability enable inherent force-control, haptic interaction, and rehabilitation application. The Barrett WAM™-aided rehabilitation training system was extensively developed in our previous work, in which passive, 5 active-assisted, 16 and resistance 17 training modes were explored. More detailed descriptions of the usages and control of the WAM™ robot could be referred in our previous work.…”

Section: Methodsmentioning

confidence: 99%

Anxiety detection and training task adaptation in robot-assisted active stroke rehabilitation

Gao

Pan

et al. 2018

International Journal of Advanced Robotic Systems

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In the therapist-centered rehabilitation program, the experienced therapists can observe emotional changes of stroke patients and make corresponding decisions on their intervention strategies. Likewise, robotic-assisted stroke rehabilitation systems will be more appreciated if they can also perceive emotional states of the stroke patients and enhance their engagements by exploring emotion-based dynamic difficulty adjustments. Nevertheless, few research have addressed this issue. A two-phase pilot study with anxiety as the target emotion state was conducted in this article. In phase I, the motor performances and the physiological responses to the stroke subject's anxiety with high, medium, and low intensities were statistically analyzed, and anxiety models with three intensities were offline developed using support vector machinebased classifiers. In phase II, anxiety-based closed-loop robot-aided training task adaptation and its impacts on patientrobot interaction engagements were explored. As a comparison, a performance-based robotic behavior adaptation was also implemented. Experimental results with 12 recruited stroke patients conducted on the Barrett WAM TM manipulator verified that the rehabilitation robot can implicitly recognize the anxiety intensities of the stroke survivors and the anxietybased real-time robotic behavior adaptation shows more engagements in the human-robot interactions.

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Section: Methodsmentioning

confidence: 99%

Anxiety detection and training task adaptation in robot-assisted active stroke rehabilitation

Gao

Pan

et al. 2018

International Journal of Advanced Robotic Systems

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“…Some studies considered the adaptation of resistance in robot-assisted rehabilitation. Guozheng Xu used the biological damping and stiffness parameters identified online to monitor the changes of muscle strength of the subjects automatically and modified the required resistance to be aligned with the changes in the muscle strength of the subjects ( Xu et al, 2017 ). OttC proposed a control framework for passive flexible joint rehabilitation robot and designed the impedance controller which was verified on the DLR lightweight robots and was only suitable for the cases of constant impedance parameters ( Albu-Schaffer et al, 2007 ).…”

Section: Introductionmentioning

confidence: 99%

Research on adaptive impedance control technology of upper limb rehabilitation robot based on impedance parameter prediction

Zhang,

Li,

Tao

et al. 2024

Front. Bioeng. Biotechnol.

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Introduction: With the aggravation of aging and the growing number of stroke patients suffering from hemiplegia in China, rehabilitation robots have become an integral part of rehabilitation training. However, traditional rehabilitation robots cannot modify the training parameters adaptively to match the upper limbs’ rehabilitation status automatically and apply them in rehabilitation training effectively, which will improve the efficacy of rehabilitation training.Methods: In this study, a two-degree-of-freedom flexible drive joint rehabilitation robot platform was built. The forgetting factor recursive least squares method (FFRLS) was utilized to estimate the impedance parameters of human upper limb end. A reward function was established to select the optimal stiffness parameters of the rehabilitation robot.Results: The results confirmed the effectiveness of the adaptive impedance control strategy. The findings of the adaptive impedance control studies showed that the adaptive impedance control had a significantly greater reward than the constant impedance control, which was in line with the simulation results of the variable impedance control. Moreover, it was observed that the levels of robot assistance could be suitably modified based on the subject’s different participation.Discussion: The results facilitated stroke patients’ upper limb rehabilitation by enabling the rehabilitation robot to adaptively change the impedance parameters according to the functional status of the affected limb. In clinic therapy, the proposed control strategy may help to adjust the reward function for different patients to improve the rehabilitation efficacy eventually.

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“…In contrast, soft exosuits made of soft materials and mechanisms are light and compliant and exhibit better portability and human interaction flexibility. However, in some cases, such as the rehabilitation training of apoplectic patients [6] and human function augmentation of industrial workers [7], wearable robots require a stable support or additional joint buffer. Therefore, the development of variable stiffness wearable robots, particularly soft exosuits, is of great importance [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Friction Prediction and Validation of a Variable Stiffness Lower Limb Exosuit Based on Finite Element Analysis

Zuo

Chen

et al. 2021

Actuators

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The variable stiffness exosuit has great potential for human augmentation and medical applications. However, the model of the variable stiffness mechanism in exosuits is far from satisfactory for the accurate prediction and control of friction force. This paper presents a friction prediction model of a variable stiffness lower limb exosuit, verifies its prediction performance, and identifies its applicability. The friction force model was established by the Coulomb friction hypothesis. The equivalent coefficient, which is the core parameter of the model, was determined based on friction and squeezing force data obtained by tests and an ANSYS simulation. Experiments show that the prediction error of the proposed model can reach 15% with a proper structural dimension change constraint. The friction force control test showed that the achieved model can shorten the settling time of the step response by 26% and eliminate the steady-state error. Verifications indicate that the proposed method can provide guidance to the modeling of other friction/stiffness structures, especially friction-based wearable robot structure models and predictions.

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A novel approach for robot-assisted upper-limb rehabilitation

Cited by 9 publications

References 24 publications

Anxiety detection and training task adaptation in robot-assisted active stroke rehabilitation

Anxiety detection and training task adaptation in robot-assisted active stroke rehabilitation

Research on adaptive impedance control technology of upper limb rehabilitation robot based on impedance parameter prediction

Friction Prediction and Validation of a Variable Stiffness Lower Limb Exosuit Based on Finite Element Analysis

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