Multi‐Arm lower‐limb rehabilitation robot for motor coordination training after stroke

Xia, Jinfeng; Wei, Jianjun; Sun, Zengqi; Duan, Li; Liu, Quanquan; Shen, Yaijing; Shang, Wanfeng; Lin, Zhifeng; Li, Jianjun; Wang, Yulong

doi:10.1049/joe.2018.9403

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…The lower computer and system status monitoring [23][24][25]. On the other hand, the operation of the lower computer software includes two aspects: on the one hand, it receives the control commands issued by the upper computer, and receives the connection code signal and the boundary switch to form a closed loop control [27][28].…”

Section: Upper Limb Rehabilitation Robot System Controlled By Internementioning

confidence: 99%

Retracted: Upper Limb Rehabilitation Robot System Based on Internet of Things Remote Control

Zhong

2020

IEEE Access

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Modern technology has been improving, as is medical technology. Over the years, rehabilitation medicine is developing and growing. The use of rehabilitation robots to achieve the upper limb motor function of patients with hemiplegia has also become a popular research in academia. Under this background, this paper proposes an upper limb robot rehabilitation system based on Internet of Things remote control. The upper limb robotic rehabilitation system based on the Internet of Things in this paper is composed of upper computer and lower computer. Information is collected by pressure sensor. The transmission process is realized by STM32 controller, which is first transmitted to the upper computer, and then the information needs to be processed After processing, it sends control commands to the lower computer controller to control the motor drive of the rehabilitation robot, so as to realize the rehabilitation training of the patient. In order to verify the reliability of the system in this paper, this paper conducted a motion test and system dynamic performance test. The research results of this paper show that the passive motion accuracy of the system in this paper has reached more than 97%, and the active motion accuracy has reached more than 98%. In addition, the maximum speed response time of the upper limb rehabilitation robot system based on the remote control of the Internet of Things in this paper is 5.7ms. The amount of adjustment is 5.32%, and the dynamic performance is good. The research results of this paper show that the upper limb rehabilitation robot system based on the Internet of Things remote control in this paper has excellent performance, which can provide a certain reference value for the research of rehabilitation robot.

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Section: Upper Limb Rehabilitation Robot System Controlled By Internementioning

confidence: 99%

Retracted: Upper Limb Rehabilitation Robot System Based on Internet of Things Remote Control

Zhong

2020

IEEE Access

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Section: Introductionmentioning

confidence: 99%

“…In this landscape, the integration of deep learning and image processing technology in assessing rehabilitation outcomes has been noted [17,18]. These technologies adeptly register the nuances of patient movements and the evolution of these actions throughout the rehabilitation tenure, laying the foundation for a more methodical evaluation [17,18]. However, despite their transformative potential in rehabilitation medicine, certain impediments and complexities are observed in their practical deployment [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Deep Image Processing for Lower Limb Rehabilitation Training Action and Effect Recognition: GaitSet Algorithm and Full-Field Optical Flow Approaches

Wang,

Yun,

Zhang

2023

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With advancements in deep learning and image processing technology, evaluations of lower limb rehabilitation training have witnessed improved accuracy and efficiency. However, traditional image processing techniques frequently neglect the inherent sequence-level characteristics during action recognition and fail to exploit the comprehensive full-field data when discerning rehabilitation training action standards. Addressing these limitations, a novel method was proposed. Initially, the GaitSet algorithm was employed to recognize the lower limb rehabilitation training actions, ensuring complete consideration of sequencelevel features. Subsequently, leveraging the full-field optical flow tracking approach, challenges associated with discerning the standards of lower limb rehabilitation training actions were examined. It is anticipated that this novel methodology can offer an enhanced tool for evaluating the effectiveness of lower limb rehabilitation training in the realm of rehabilitation medicine. Such advancements could potentially contribute to optimizing rehabilitation outcomes and augmenting patients' quality of life.

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“…Advantages have been obtained for using robots to train limb motor function. [3][4][5][6] In order to restore the motor ability of lower limbs, a rehabilitation robot targeting to secondary gait deviations was developed, which could affect the motion mode of stroke survivor pelvis. 7 A 6-DOF robot with upper limb device and lower limb device was developed.…”

Section: Introductionmentioning

confidence: 99%

Design and evaluation of a double rope rehabilitation robot for sit-to-stand training

Cao

Inoue

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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A double rope rehabilitation robot for sit-to-stand training is developed for human lower limb rehabilitations. Two kinds of training methods are used to ensure the safety control of rehabilitation activities. During the sit-to-stand training, the robot sensor system measures the ground reaction force, center of pressure, tension of ropes, motion track of body trunk, and the rotation angle of each body segment, and then the control system calculates the joint torques of hip, knee and ankle in real time. Six volunteers are involved in the experiment, and the results verify that the rehabilitation robot can ensure the completion of natural track in sit-to-stand training and improve the joint torque. In addition, the user’s motor intention can be identified by the control system, and the lower limb motor function can be trained effectively by encouraging users to exert strength by themselves. Therefore, the developed rehabilitation robot can be used to evaluate human body dynamics parameters and may improve rehabilitation activities in clinical environment.

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Multi‐Arm lower‐limb rehabilitation robot for motor coordination training after stroke

Cited by 9 publications

References 21 publications

Retracted: Upper Limb Rehabilitation Robot System Based on Internet of Things Remote Control

Retracted: Upper Limb Rehabilitation Robot System Based on Internet of Things Remote Control

Deep Image Processing for Lower Limb Rehabilitation Training Action and Effect Recognition: GaitSet Algorithm and Full-Field Optical Flow Approaches

Design and evaluation of a double rope rehabilitation robot for sit-to-stand training

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