Biomimetic Impedance Control of an EMG-Based Robotic Hand

Tsuji, Takao; Shima, Keisuke; Bu, Nan; Fukuda, Osamu

doi:10.5772/9184

Cited by 14 publications

(15 citation statements)

References 18 publications

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“…List of impedance parameters used in the experiments. Each parameter was determined based on the experimental results of the human wrist impedance measurement (49).…”

Section: Motion Number Mmentioning

confidence: 99%

A myoelectric prosthetic hand with muscle synergy–based motion determination and impedance model–based biomimetic control

et al. 2019

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Prosthetic hands are prescribed to patients who have suffered an amputation of the upper limb due to an accident or a disease. This is done to allow patients to regain functionality of their lost hands. Myoelectric prosthetic hands were found to have the possibility of implementing intuitive controls based on operator’s electromyogram (EMG) signals. These controls have been extensively studied and developed. In recent years, development costs and maintainability of prosthetic hands have been improved through three-dimensional (3D) printing technology. However, no previous studies have realized the advantages of EMG-based classification of multiple finger movements in conjunction with the introduction of advanced control mechanisms based on human motion. This paper proposes a 3D-printed myoelectric prosthetic hand and an accompanying control system. The muscle synergy–based motion-determination method and biomimetic impedance control are introduced in the proposed system, enabling the classification of unlearned combined motions and smooth and intuitive finger movements of the prosthetic hand. We evaluate the proposed system through operational experiments performed on six healthy participants and an upper-limb amputee participant. The experimental results demonstrate that our prosthetic hand system can successfully classify both learned single motions and unlearned combined motions from EMG signals with a high degree of accuracy. Furthermore, applications to real-world uses of prosthetic hands are demonstrated through control tasks conducted by the amputee participant.

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“…List of impedance parameters used in the experiments. Each parameter was determined based on the experimental results of the human wrist impedance measurement (49).…”

Section: Motion Number Mmentioning

confidence: 99%

A myoelectric prosthetic hand with muscle synergy–based motion determination and impedance model–based biomimetic control

et al. 2019

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“…The imitation approaches are applied to each of the J demonstrations, and evaluated by computing the average cost accumulated in each trajectory [according to the true expert cost (33)]. The results are summarized in Table I, and examples of the imitated behavior are plotted in Fig.…”

Section: Left) Its Equation Of Motion Ismentioning

confidence: 99%

“…For evaluation, we compare the behavior of the robot when imitating behavior through 1) the inverse OC approach and, 2) the direct imitation approach. For the former, ILQG is used to find the optimal controller for the Edinburgh SEA with respect to the cost function [i.e., (33), using the learned weights]. For the latter, the human EMG signals are scaled according to the maximum admissible commanded angle of the robot motors, and then fed directly as commands to the robot, i.e., drawing the correspondence eû ≡ lû , where eû and lû are the commands normalized by the admissible ranges for the human and robot, respectively.…”

Section: Inverse Optimal Control From Human Datamentioning

confidence: 99%

Transferring Human Impedance Behavior to Heterogeneous Variable Impedance Actuators

2013

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Abstract-This paper presents a comparative study of approaches to control robots with variable impedance actuators (VIAs) in ways that imitate the behavior of humans. We focus on problems where impedance modulation strategies are recorded from human demonstrators for transfer to robotic systems with differing levels of heterogeneity, both in terms of the dynamics and actuation. We categorize three classes of approach that may be applied to this problem, namely, 1) direct, 2) feature-based, and 3) inverse optimal approaches to transfer. While the first is restricted to highly biomorphic plants, the latter two are shown to be sufficiently general to be applied to various VIAs in a way that is independent of the mechanical design. As instantiations of such transfer schemes, 1) a constraint-based method and 2) an apprenticeship learning framework are proposed, and their suitability to different problems in robotic imitation, in terms of efficiency, ease of use, and task performance, is characterized. The approaches are compared in simulation on systems of varying complexity, and robotic experiments are reported for transfer of behavior from human electromyographic data to two different variable passive compliance robotic devices.

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“…A number of studies on myoelectric prosthetic hands [1][2][3][4][5][6][7] which are controlled based on electromyogram (EMG) signals, have been conducted to support the daily living of upper-limb amputees. However, to control these prostheses naturally, several months of training under the instruction of doctors and therapists are required.…”

Section: Introductionmentioning

confidence: 99%

A virtual myoelectric prosthesis training system capable of providing instructions on hand operations

Nakamura

Shibanoki

Kurita

et al. 2017

International Journal of Advanced Robotic Systems

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This article proposes a virtual hand and a virtual training system for controlling the MyoBock-the most commonly used myoelectric prosthetic hand worldwide. As the virtual hand is controlled using the method also adopted for the MyoBock hand, the proposed system provides upper-limb amputees with operation sensibilities similar to those experienced in MyoBock control. It can also display an additional virtual hand for the provision of instructions on hand operation, such as the recommended posture for object grasping and the trajectory desirable to reach a target. In virtual hand control experiments conducted with an amputee to evaluate the proposed virtual hand's operability, the subject successfully performed stable opening and closing with high discrimination rates ð89:3+6:65%Þ, thanks to the virtual hand's incorporation of the MyoBock's operational characteristics. A training experiment using the proposed system was also conducted with eight healthy participants over a period of 5 days. The participants were asked to perform the box and block test using the MyoBock hand in a real environment on the first and final days. The results showed that the number of blocks transported in 1 min significantly increased and that the participants using the instruction virtual hand changed the orientation of the hand approaching blocks from vertical to lateral. The outcomes of the experiment indicate that the proposed system can be used to improve MyoBock hand control operation both quantitatively and qualitatively.

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Biomimetic Impedance Control of an EMG-Based Robotic Hand

Cited by 14 publications

References 18 publications

A myoelectric prosthetic hand with muscle synergy–based motion determination and impedance model–based biomimetic control

A myoelectric prosthetic hand with muscle synergy–based motion determination and impedance model–based biomimetic control

Transferring Human Impedance Behavior to Heterogeneous Variable Impedance Actuators

A virtual myoelectric prosthesis training system capable of providing instructions on hand operations

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