Human-In-The-Loop Control and Task Learning for Pneumatically Actuated Muscle Based Robots

Teramae, Tatsuya; Ishihara, Koji; Babič, Jan; Morimoto, Jun; Öztop, Erhan

doi:10.3389/fnbot.2018.00071

Cited by 12 publications

(3 citation statements)

References 28 publications

(38 reference statements)

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“…Hence, to regain the upper limb motion functionality, it may be required to consider the simultaneous activation of multiple DOFs. However, in other human–robot collaboration systems, such as the manipulation of a robot in intelligent manufacturing [ 25 ] or skill transfer [ 26 , 27 ], the hands alone can be in direct contact with the robot. Figure 1 illustrates an example of the application of human–robot collaboration in manufacturing based on human hand trajectory prediction.…”

Section: Introductionmentioning

confidence: 99%

EMG-Based 3D Hand Motor Intention Prediction for Information Transfer from Human to Robot

Feleke

Fei

2021

Sensors

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(1) Background: Three-dimensional (3-D) hand position is one of the kinematic parameters that can be inferred from Electromyography (EMG) signals. The inferred parameter is used as a communication channel in human–robot collaboration applications. Although its application from the perspective of rehabilitation and assistive technologies are widely studied, there are few papers on its application involving healthy subjects such as intelligent manufacturing and skill transfer. In this regard, for tasks associated with complex hand trajectories without the consideration of the degree of freedom (DOF), the prediction of 3-D hand position from EMG signal alone has not been addressed. (2) Objective: The primary aim of this study is to propose a model to predict human motor intention that can be used as information from human to robot. Therefore, the prediction of a 3-D hand position directly from the EMG signal for complex trajectories of hand movement, without the direct consideration of joint movements, is studied. In addition, the effects of slow and fast motions on the accuracy of the prediction model are analyzed. (3) Methods: This study used the EMG signal that is collected from the upper limb of healthy subjects, and the position signal of the hand while the subjects manipulate complex trajectories. We considered and analyzed two types of tasks with complex trajectories, each with quick and slow motions. A recurrent fuzzy neural network (RFNN) model was constructed to predict the 3-D position of the hand from the features of EMG signals alone. We used the Pearson correlation coefficient (CC) and normalized root mean square error (NRMSE) as performance metrics. (4) Results: We found that 3-D hand positions of the complex movement can be predicted with the mean performance of CC = 0.85 and NRMSE = 0.105. The 3-D hand position can be predicted well within a future time of 250 ms, from the EMG signal alone. Even though tasks performed under quick motion had a better prediction performance; the statistical difference in the accuracy of prediction between quick and slow motion was insignificant. Concerning the prediction model, we found that RFNN has a good performance in decoding for the time-varying system. (5) Conclusions: In this paper, irrespective of the speed of the motion, the 3-D hand position is predicted from the EMG signal alone. The proposed approach can be used in human–robot collaboration applications to enhance the natural interaction between a human and a robot.

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Section: Introductionmentioning

confidence: 99%

EMG-Based 3D Hand Motor Intention Prediction for Information Transfer from Human to Robot

Feleke

Fei

2021

Sensors

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“…In the case of excessive movement or sudden abnormal force, it is easy to cause secondary injury to the patient's affected limb. Compared with rigid motors, pneumatic muscle actuators (PMAs) have advantages of good safety, high compliance, light weight and low cost [10]. Tang et al [11] developed a 1-DOF upper limb assistive device for elbow flexion/extension using PMAs.…”

Section: Introductionmentioning

confidence: 99%

Design and Control of a Reconfigurable Upper Limb Rehabilitation Exoskeleton With Soft Modular Joints

Liu

Zhu

et al. 2021

IEEE Access

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Upper limb rehabilitation robot can effectively help patients recover motor ability. Existing rehabilitation robots are usually driven by rigid motors and the mechanical structures cannot adapt to the different patients with the different physical parameters and different rehabilitation needs. This paper designs a reconfigurable upper limb rehabilitation exoskeleton for elbow and wrist joints driven by pneumatic muscle actuators (PMAs). The exoskeleton can assist patients to achieve elbow flexion/extension, wrist flexion/ extension and adduction/abduction by integrating soft elbow and wrist joint modules which can work separately or together. The wrist joint can realize two degrees of freedom (2-DoF) movement via adjustable modules. To conquer the dynamic model errors and load disturbances when reconstructing the modular joints, a non-singular fast terminal sliding mode control method based on nonlinear disturbance observer (NFTSMC-NDO) is proposed, and a position/force hierarchical control method is formed to ensure the controllability of the soft modular robot. Experimental results show that the proposed method can achieve high-precision motion control of soft modular joints and provide reconfigurable assistance for patients, improving the adaptability and compliance of rehabilitation training.

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“…In addition to the general advantages of soft robotics, soft actuators, such as contraction and extension pneumatic muscle actuators (PMAs), have their benefits when compared with the traditional electrical and mechanical actuators. Moreover, there is a high ratio of force to the actuator weight, in most cases a 100 newtons for several 100 g (Tondu and Lopez, 2000;Al-Ibadi et al, 2017, 2018aYang et al, 2019), but on the other hand, due to the softness, low stiffness, and hysteresis, the PMA shows a high degree of non-linearity and adds more challenges to controlling such types of actuators (Wang et al, 2017;Giannaccini et al, 2018;Teramae et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Controlling of Pneumatic Muscle Actuator Systems by Parallel Structure of Neural Network and Proportional Controllers (PNNP)

2020

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This article proposed a novel controller structure to track the non-linear behavior of the pneumatic muscle actuator (PMA), such as the elongation for the extensor actuator and bending for the bending PMA. The proposed controller consists of a neural network (NN) controller laid in parallel with the proportional controller (P). The parallel neural network proportional (PNNP) controllers provide a high level of precision and fast-tracking control system. The PNNP has been applied to control the length of the single extensor PMA and the bending angle of the single self-bending contraction actuator (SBCA) at different load values. For further validation, the PNNP has been applied to control a human-robot shared control system. The results show the efficiency of the proposed controller structure.

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Human-In-The-Loop Control and Task Learning for Pneumatically Actuated Muscle Based Robots

Cited by 12 publications

References 28 publications

EMG-Based 3D Hand Motor Intention Prediction for Information Transfer from Human to Robot

EMG-Based 3D Hand Motor Intention Prediction for Information Transfer from Human to Robot

Design and Control of a Reconfigurable Upper Limb Rehabilitation Exoskeleton With Soft Modular Joints

Controlling of Pneumatic Muscle Actuator Systems by Parallel Structure of Neural Network and Proportional Controllers (PNNP)

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