Design and control of a cable-driven rehabilitation robot for upper and lower limbs

Oyman, Efe Levent; Korkut, Muhammed Yusuf; Ylmaz, Cüneyt; Bayraktaroglu, Zeki Y.; Arslan, M. Selçuk

doi:10.1017/s0263574721000357

Cited by 13 publications

(6 citation statements)

References 48 publications

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“…Stroke is a significant disease that seriously affects people's physical and mental health globally [1]. Robot-aided rehabilitation is a novel solution to the increasing demand for stroke survivors with motor dysfunction [2][3][4][5][6]. However, continuous passive motion-based therapy does not generate significant improvement for post-stroke patients [7], and active movement is considered to be one of the most effective approaches to upper limb function restoration [8].…”

Section: Introductionmentioning

confidence: 99%

Bayesian optimization for assist-as-needed controller in robot-assisted upper limb training based on energy information

Zhang

Zeng

et al. 2023

Robotica

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The assist-as-needed (AAN) controller is effective in robot-assisted rehabilitation. However, variations of the engagement of subjects with fixed controller often lead to unsatisfying results. Therefore, adaptive AAN that adjusts control parameters based on individualized engagement is essential to enhance the training effect further. Nevertheless, current approaches mainly focus on the within-trial real-time engagement estimation, and the presence of measurement noise may cause improper evaluation of engagement. In addition, most studies on human-in-loop optimization strategies modulate the controller by greedy strategies, which are prone to fall into local optima. These shortcomings in previous studies could significantly limit the efficacy of AAN. This paper proposes an adaptive AAN to promote engagement by providing subjects with a subject-adaptive assistance level based on trial-wise engagement estimation and performance. Firstly, the engagement is estimated from energy information, which assesses the work done by the subject during a full trial to reduce the influence of measurement outliers. Secondly, the AAN controller is adapted by Bayesian optimization (BO) to maximize the subject’s performance according to historical trial-wise performance. The BO algorithm is good at dealing with noisy signals within limited steps. Experiments with ten healthy subjects resulted in a decrease of 34.59 $\%$ in their average trajectory error, accompanied by a reduction of 9.71 $\%$ in their energy consumption, thus verifying the superiority of the proposed method to prior work. These results suggest that the proposed method could potentially improve the effect of upper limb rehabilitation.

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Section: Introductionmentioning

confidence: 99%

Bayesian optimization for assist-as-needed controller in robot-assisted upper limb training based on energy information

Zhang

Zeng

et al. 2023

Robotica

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“…In refs. [9,10], an experimental control of a planar CDPR is performed with a robust PID controller, and impedance and PD controller are implemented to control cables on the rehabilitation robot. The sliding mode control method, which can handle the changes in system parameters, disturbing effects, and uncertainties, is employed in refs.…”

Section: Introductionmentioning

confidence: 99%

Position control of a planar cable-driven parallel robot using reinforcement learning

2022

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This study proposes a method based on reinforcement learning (RL) for point-to-point and dynamic reference position tracking control of a planar cable-driven parallel robots, which is a multi-input multi-output system (MIMO). The method eliminates the use of a tension distribution algorithm in controlling the system’s dynamics and inherently optimizes the cable tensions based on the reward function during the learning process. The deep deterministic policy gradient algorithm is utilized for training the RL agents in point-to-point and dynamic reference tracking tasks. The performances of the two agents are tested on their specifically trained tasks. Moreover, we also implement the agent trained for point-to-point tasks on the dynamic reference tracking and vice versa. The performances of the RL agents are compared with a classical PD controller. The results show that RL can perform quite well without the requirement of designing different controllers for each task if the system’s dynamics is learned well.

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“…This is usually achieved by developing three different operational modes, namely active training, passive training, and assistive training. Preliminary work on such control schemes has been presented in [30,31], but it is only applied to designs with a static platform, rather than wearable ones.…”

Section: Introductionmentioning

confidence: 99%

Control Design for CABLEankle, a Cable Driven Manipulator for Ankle Motion Assistance

2022

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A control design is presented for a cable driven parallel manipulator for performing a controlled motion assistance of a human ankle. Requirements are discussed for a portable, comfortable, and light-weight solution of a wearable device with an overall design with low-cost features and user-oriented operation. The control system utilizes various operational and monitoring sensors to drive the system and also obtain continuous feedback during motion to ensure an effective recovery. This control system for CABLEankle device is designed for both active and passive rehabilitation to facilitate the improvement in both joint mobility and surrounding muscle strength.

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Design and control of a cable-driven rehabilitation robot for upper and lower limbs

Cited by 13 publications

References 48 publications

Bayesian optimization for assist-as-needed controller in robot-assisted upper limb training based on energy information

Bayesian optimization for assist-as-needed controller in robot-assisted upper limb training based on energy information

Position control of a planar cable-driven parallel robot using reinforcement learning

Control Design for CABLEankle, a Cable Driven Manipulator for Ankle Motion Assistance

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