Design and Workspace Analysis of a Parallel Ankle Rehabilitation Robot (PARR)

Zhang, Leiyu; Li, Jianfeng; Dong, Mingjie; Fang, Bin; Chen, Ying; Zuo, Shuguang; Zhang, Kai

doi:10.1155/2019/4164790

Cited by 36 publications

(14 citation statements)

References 20 publications

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“…Low, real-time and cost-effective. Use the man-machine interface on the computer (upper computer position) to select different operation modes, and transmit the information to the STM32 controller (lower computer) through serial communication to control the motor to work, so as to drive the mechanical arm to move, and in the same At the time, the position information collected from the encoder and the torque information collected from the torque sensor are fed back to the computer through the STM32 controller [29], which can realize the evaluation, analysis and guidance of patient rehabilitation training [30][31]. As can be seen from Figure 4, the upper limb rehabilitation IoT is the application of parameter information collected by various sensors as parameter variables to the automatically controlled IoT, providing a scientific basis for upper limb rehabilitation robots for data information and video information transmission, and server-side remote control The purpose of client patients for rehabilitation training [32,33].…”

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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“…4n [37,38], which can also be changed into a 3-RUS/U redundant mechanism with 2-DOFs when the redundant actuator of the constraint limb is self-locked [39]; the 3-DOF 3-PRS parallel manipulator for ankle rehabilitation shown in Fig. 4o [40][41][42]; and the 3-DOF 2-UPS/RRR PARR shown in Fig. 4p [43][44][45][46]. For all these PARRs, the most commonly used actuator of pneumatic-driven PARRs is the pneumatic muscle actuator (PMA) due to its superiority in terms of low weight and high power/volume ratios, which can provide an intrinsic softness to make joint compliance possible [47,70].…”

Section: Mechanism Configurationsmentioning

confidence: 99%

“…Therefore, a designed PARR must have a sufficient workspace and excellent kinematic performance, such as velocity transfer performance and force transfer performance. Because the AJC has 3-DOFs, many PARRs are designed with 3-DOFs, such as the PARRs in [24,32,34,35,37,40,[43][44][45], to satisfy all the motion of the AJC. Some PARRs are designed to have only 2-DOFs to simplify the mechanism by eliminating the AD/AB motion, such as the PARRs in [25,55].…”

Section: Similarities Among the Reviewed Parrs Mechanical Design And mentioning

confidence: 99%

State of the art in parallel ankle rehabilitation robot: a systematic review

Dong

Zhou

et al. 2021

J NeuroEngineering Rehabil

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Background The ankle joint complex (AJC) is of fundamental importance for balance, support, and propulsion. However, it is particularly susceptible to musculoskeletal and neurological injuries, especially neurological injuries such as drop foot following stroke. An important factor in ankle dysfunction is damage to the central nervous system (CNS). Correspondingly, the fundamental goal of rehabilitation training is to stimulate the reorganization and compensation of the CNS, and to promote the recovery of the motor system’s motor perception function. Therefore, an increasing number of ankle rehabilitation robots have been developed to provide long-term accurate and uniform rehabilitation training of the AJC, among which the parallel ankle rehabilitation robot (PARR) is the most studied. The aim of this study is to provide a systematic review of the state of the art in PARR technology, with consideration of the mechanism configurations, actuator types with different trajectory tracking control techniques, and rehabilitation training methods, thus facilitating the development of new and improved PARRs as a next step towards obtaining clinical proof of their rehabilitation benefits. Methods A literature search was conducted on PubMed, Scopus, IEEE Xplore, and Web of Science for articles related to the design and improvement of PARRs for ankle rehabilitation from each site’s respective inception from January 1999 to September 2020 using the keywords “ parallel”, “ ankle”, and “ robot”. Appropriate syntax using Boolean operators and wildcard symbols was utilized for each database to include a wider range of articles that may have used alternate spellings or synonyms, and the references listed in relevant publications were further screened according to the inclusion criteria and exclusion criteria. Results and discussion Ultimately, 65 articles representing 16 unique PARRs were selected for review, all of which have developed the prototypes with experiments designed to verify their usability and feasibility. From the comparison among these PARRs, we found that there are three main considerations for the mechanical design and mechanism optimization of PARRs, the choice of two actuator types including pneumatic and electrically driven control, the covering of the AJC’s motion space, and the optimization of the kinematic design, actuation design and structural design. The trajectory tracking accuracy and interactive control performance also need to be guaranteed to improve the effect of rehabilitation training and stimulate a patient’s active participation. In addition, the parameters of the reviewed 16 PARRs are summarized in detail with their differences compared by using figures and tables in the order they appeared, showing their differences in the two main actuator types, four exercise modes, fifteen control strategies, etc., which revealed the future research trends related to the improvement of the PARRs. Conclusion The selected studies showed the rapid development of PARRs in terms of their mechanical designs, control strategies, and rehabilitation training methods over the last two decades. However, the existing PARRs all have their own pros and cons, and few of the developed devices have been subjected to clinical trials. Designing a PARR with three degrees of freedom (DOFs) and whereby the mechanism’s rotation center coincides with the AJC rotation center is of vital importance in the mechanism design and optimization of PARRs. In addition, the design of actuators combining the advantages of the pneumatic-driven and electrically driven ones, as well as some new other actuators, will be a research hotspot for the development of PARRs. For the control strategy, compliance control with variable parameters should be further studied, with sEMG signal included to improve the real-time performance. Multimode rehabilitation training methods with multimodal motion intention recognition, real-time online detection and evaluation system should also be further developed to meet the needs of different ankle disability and rehabilitation stages. In addition, the clinical trials are in urgent need to help the PARRs be implementable as an intervention in clinical practice.

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“…Details of mechanical design of the developed PARR are shown in our previous work [ 25 ]. During the ankle isokinetic muscle strength exercise, only the movements of DO/PL and IN/EV are usually considered.…”

Section: Inverse Solution Of Positions Of the Parrmentioning

confidence: 99%

Kinematic Calibration of a Parallel 2-UPS/RRR Ankle Rehabilitation Robot

Dong

Kong

et al. 2020

Journal of Healthcare Engineering

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In order to better perform rehabilitation training on the ankle joint complex in the direction of dorsiflexion/plantarflexion and inversion/eversion, especially when performing the isokinetic muscle strength exercise, we need to calibrate the kinematic model to improve its control precision. The ankle rehabilitation robot we develop is a parallel mechanism, with its movements in the two directions driven by two linear motors. Inverse solution of positions is deduced and the output lengths of the two UPS kinematic branches are calibrated in the directions of dorsiflexion, plantarflexion, inversion, and eversion, respectively. Motion of each branch in different directions is fitted in high-order form according to experimental data. Variances, standard deviation, and goodness of fit are taken into consideration when choosing the best fitting curve, which ensures that each calibration can match the most appropriate fitting curve. Experiments are conducted to verify the effectiveness of the kinematic calibration after finishing the calibration, and the errors before and after calibration of the two kinematic chains in different directions are compared, respectively, which shows that the accuracy after calibration has been significantly improved.

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Design and Workspace Analysis of a Parallel Ankle Rehabilitation Robot (PARR)

Cited by 36 publications

References 20 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

State of the art in parallel ankle rehabilitation robot: a systematic review

Kinematic Calibration of a Parallel 2-UPS/RRR Ankle Rehabilitation Robot

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