Implementation of passive compliance training on a parallel ankle rehabilitation robot to enhance safety

Li, Jianfeng; Fan, Wenpei; Dong, Mingjie; Rong, Xi

doi:10.1108/ir-02-2020-0040

Cited by 21 publications

(13 citation statements)

References 27 publications

(30 reference statements)

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“…However, the robot suffers from a problem whereby unexpected loads may be exerted on a patient's foot when rehabilitation begins [37]. For the 3-DOF 2-UPS/RRR PARR, the passive rehabilitation training trajectories were preset with the control system built [65,66], and three compliance rehabilitation training strategies developed based on admittance control and its derivatives, namely, patient-passive compliance exercise, isotonic exercise, and patient-active exercise, were developed to enhance the training safety, fully considering the patient's muscle strength level and covering different stages of recovery with good compliance [67].…”

Section: Rehabilitation Training Methodsmentioning

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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Section: Rehabilitation Training Methodsmentioning

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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“…The kinematic coordinate system of the parallel 2-UPS/RRR rehabilitation robot is shown as in Figure 3(a) [ 26 ]. The fixed coordinate system O − X o Y o Z o and the moving coordinate system M − X m Y m Z m are established at the rotation center of the mechanism coinciding at the initial position, all along the directions of three rotation axes.…”

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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“…Recently, in order to assist and improve the process of ankle rehabilitation, some firms have developed several commercial electromechanical systems that allow patients to move and stretch the muscles and tendons gently [29][30][31][32][33]; usually their movements are similar to the basic ankle movements (figure 4), being able to obtain different ROM (measured in degrees) and various angular velocities (measured in degrees/s) for each rotation axis considered. Table (1) summarizes the ranges of ankle movements [39] and the maximum passive moments [40,41,42].…”

Section: Intermediate Complexity Devicesmentioning

confidence: 99%

“…For more recent approaches for ankle rehabilitation using PRs, readers may refer to [38][39][40][41][42]. The selection and design of the control algorithms are based on analysis of the rehabilitation protocol taking into account the dynamics of both the system and the human-robot interaction.…”

Section: Introductionmentioning

confidence: 99%

Passive Exercise Adaptation for Ankle Rehabilitation Based on Learning Control Framework

Abu‐Dakka

Valera

Escalera

et al. 2020

Sensors

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Ankle injuries are among the most common injuries in sport and daily life. However, for their recovery, it is important for patients to perform rehabilitation exercises. These exercises are usually done with a therapist’s guidance to help strengthen the patient’s ankle joint and restore its range of motion. However, in order to share the load with therapists so that they can offer assistance to more patients, and to provide an efficient and safe way for patients to perform ankle rehabilitation exercises, we propose a framework that integrates learning techniques with a 3-PRS parallel robot, acting together as an ankle rehabilitation device. In this paper, we propose to use passive rehabilitation exercises for dorsiflexion/plantar flexion and inversion/eversion ankle movements. The therapist is needed in the first stage to design the exercise with the patient by teaching the robot intuitively through learning from demonstration. We then propose a learning control scheme based on dynamic movement primitives and iterative learning control, which takes the designed exercise trajectory as a demonstration (an input) together with the recorded forces in order to reproduce the exercise with the patient for a number of repetitions defined by the therapist. During the execution, our approach monitors the sensed forces and adapts the trajectory by adding the necessary offsets to the original trajectory to reduce its range without modifying the original trajectory and subsequently reducing the measured forces. After a predefined number of repetitions, the algorithm restores the range gradually, until the patient is able to perform the originally designed exercise. We validate the proposed framework with both real experiments and simulation using a Simulink model of the rehabilitation parallel robot that has been developed in our lab.

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Implementation of passive compliance training on a parallel ankle rehabilitation robot to enhance safety

Cited by 21 publications

References 27 publications

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

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

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

Passive Exercise Adaptation for Ankle Rehabilitation Based on Learning Control Framework

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