Dual-Modal Hybrid Control for an Upper-Limb Rehabilitation Robot

Gao, Feng; Zhang, Jiaji; Zuo, Guokun; Li, Maoqin; Jiang, Dexin; Yang, Lei

doi:10.3390/machines10050324

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…The previous research [5] proposed a passive training trajectory planning method, which did not consider the patient's participation during the training process and could not adjust the passive training parameters according to the patient's actual motor ability. The paper [15] proposed to use the potential field method and ADRC position control to plan the trajectory and assist the patient in training, which required the patient to have a certain movement ability to complete the training. Compared to these methods, our hybrid active-passive training proposed improves the active participation of patients in the passive training mode.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Parametric Adaptive Hybrid Active–Passive Training Control Method for Rehabilitation Robot

Zhuang

Zhu

et al. 2022

Machines

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Rehabilitation robots facilitate patients to take part in physical and occupational training. Most of the rehabilitation robots used in clinical practice adopt pure passive training or active training, which cannot sense the active participation of patients during passive training and lack adaptive dynamic adjustment of training parameters for patients. In this paper, an intelligent hybrid active–passive training control method is proposed to enhance the active participation of patients in passive training mode. Firstly, the patients’ joint mobility and maximum muscle power are modelized and calibrated. Secondly, the robot joints are actuated to train according to joint mobility and speed for two cycles. The human–machine coupled force interaction control model can recognize the patients’ active participation in the training process. Finally, the passive training joint motion speed for the next training cycle is adaptively updated by the proposed control method. The experimental results demonstrate that the control method can sense the patients’ active participation and adjust the passive training speed according to the patients’ active force interaction. In conclusion, the hybrid active–passive training control method proposed in this paper achieves the desired goal and effectively improves the patients’ rehabilitation effect.

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Section: Discussionmentioning

confidence: 99%

Intelligent Parametric Adaptive Hybrid Active–Passive Training Control Method for Rehabilitation Robot

Zhuang

Zhu

et al. 2022

Machines

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“…However, a more prominent advantage is that robots can provide flexible and precise rehabilitation training, which can enhance the effectiveness of patients' rehabilitation. The main example of this is that the rehabilitation robot can combine modern multimedia and interactive technology in patients' training to stimulate their interest and enthusiasm in participating in the rehabilitation training, and to mobilize their awareness and ability to actively participate in the training, which, thus, promotes the recovery and compensation of patients' neurological functions [12].…”

Section: The Value Of Robot-assisted Technology In the Motor Rehabili...mentioning

confidence: 99%

The Use of Sports Rehabilitation Robotics to Assist in the Recovery of Physical Abilities in Elderly Patients with Degenerative Diseases: A Literature Review

Wang

Xie

et al. 2023

Healthcare

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The increase in the number of elderly patients with degenerative diseases has brought additional medical and financial pressures, which are adding to the burden on society. The development of sports rehabilitation robotics (SRR) is becoming increasingly sophisticated at the technical level of its application; however, few studies have analyzed how it works and how effective it is in aiding rehabilitation, and fewer individualized exercise rehabilitation programs have been developed for elderly patients. The purpose of this study was to analyze the working methods and the effects of different types of SRR and then to suggest the feasibility of applying SRR to enhance the physical abilities of elderly patients with degenerative diseases. The researcher’s team searched 633 English-language journal articles, which had been published over the past five years, and they selected 38 of them for a narrative literature review. Our summary found the following: (1) The current types of SRR are generally classified as end-effector robots, smart walkers, intelligent robotic rollators, and exoskeleton robots—exoskeleton robots were found to be the most widely used. (2) The current working methods include assistant tools as the main intermediaries—i.e., robots assist patients to participate; patients as the main intermediaries—i.e., patients dominate the assistant tools to participate; and sensors as the intermediaries—i.e., myoelectric-driven robots promote patient participation. (3) Better recovery was perceived for elderly patients when using SRR than is generally achieved through the traditional single-movement recovery methods, especially in strength, balance, endurance, and coordination. However, there was no significant improvement in their speed or agility after using SRR.

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“…The design of the EBULRR platform is shown in Figure 1 [39], which has three degrees of freedom and is capable of conducting rehabilitation movements in three-dimensional space. The EBULRR consists of two mechanical arms, one of which is named the Healthy Side Manipulator (HSM) and the other is named the Affected Side Manipulator (ASM).…”

Section: Mechanical Design Of the Ebulrrmentioning

confidence: 99%

Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot

Jing-yan

et al. 2023

Sensors

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Robot-assisted rehabilitation therapy has been proven to effectively improve upper-limb motor function in stroke patients. However, most current rehabilitation robotic controllers will provide too much assistance force and focus only on the patient’s position tracking performance while ignoring the patient’s interactive force situation, resulting in the inability to accurately assess the patient’s true motor intention and difficulty stimulating the patient’s initiative, thus negatively affecting the patient’s rehabilitation outcome. Therefore, this paper proposes a fuzzy adaptive passive (FAP) control strategy based on subjects’ task performance and impulse. To ensure the safety of subjects, a passive controller based on the potential field is designed to guide and assist patients in their movements, and the stability of the controller is demonstrated in a passive formalism. Then, using the subject’s task performance and impulse as evaluation indicators, fuzzy logic rules were designed and used as an evaluation algorithm to quantitively assess the subject’s motor ability and to adaptively modify the stiffness coefficient of the potential field and thus change the magnitude of the assistance force to stimulate the subject’s initiative. Through experiments, this control strategy has been shown to not only improve the subject’s initiative during the training process and ensure their safety during training but also enhance the subject’s motor learning ability.

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Dual-Modal Hybrid Control for an Upper-Limb Rehabilitation Robot

Cited by 9 publications

References 31 publications

Intelligent Parametric Adaptive Hybrid Active–Passive Training Control Method for Rehabilitation Robot

Intelligent Parametric Adaptive Hybrid Active–Passive Training Control Method for Rehabilitation Robot

The Use of Sports Rehabilitation Robotics to Assist in the Recovery of Physical Abilities in Elderly Patients with Degenerative Diseases: A Literature Review

Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot

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