Experimental Study of an EMG-Controlled 5-DOF Anthropomorphic Prosthetic Hand for Motion Restoration

Yang, Dapeng; Jiang, Li; Huang, Qi; Liu, Rongqiang; Liu, Hong

doi:10.1007/s10846-014-0037-6

Cited by 26 publications

(18 citation statements)

References 30 publications

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“…This control strategy is robust for commercial myoelectric two-configuration grippers. Unfortunately for advanced robotic prostheses this control strategy not only limits the DOFs but also requires non-intuitive Morse code like sequences to obtain multiple DOFs, which requires an extra mental interpretation between the intended gesture to the open-close control command (Yang et al 2014).…”

Section: Surface Electromyographymentioning

confidence: 99%

“…It has been determined that most functional activities of daily living can be performed through four primary grips: power (also known as force), tripod, finger point, and the key grips (Peerdeman et al 2011;Yang et al 2014). In this study three different grip sets are examined.…”

Section: Experimental Protocol 41 Protocolmentioning

confidence: 99%

“…11 grip patterns. Grip patterns 3 to 6 are considered the primary grip patterns (Peerdeman et al 2011;Yang et al 2014). Grip patterns 1 to 4 constitute the opposed-thumb primary grips.…”

Section: Trial 3: Emg Threshold Force Levelsmentioning

confidence: 99%

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A Multi-sensor Approach for Biomimetic Control of a Robotic Prosthetic Hand

Ghataurah

Ferigo

Merhi

et al. 2017

Bioinformatics and Biomedical Engineering

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Abstract. Robotic prosthetic hands with five digits have become commercially available however their use is limited to a few grip patterns due to the unnatural and unreliable human-machine interface (HMI). The research community has addressed this problem extensively by investigating Pattern Recognition (PR) based surface-electromyography (sEMG) control. This control strategy has been recently commercialized however has yet to show clinical adoption. One of the reasons identified in the literature is due to the sEMG signals that are affected by sweating, electrode shift, ambient noise, fatigue, cross-talk between adjacent muscles, signal drifting, and force level variation. Hence recently the scientific community has started proposing multi-modal sensing techniques as a solution.This study aims to investigate the use of multi-modal sensor approach to control a robotic prosthetic hand by investigating the sparsely studied sensing mechanism called Force Myography (FMG) as a synergist to the conventional technique of sEMG. FMG uses pressure sensors on the surface of a limb to detect the volumetric changes in the underlying musculotendinous complex. This paper presents a custom prosthetic prototype instrumented with sEMG and FMG sensors and tested by a participant with a transradial amputation. Results demonstrate that this multi-sensor approach has the potential to be a valid HMI for prosthesis control.

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Section: Surface Electromyographymentioning

confidence: 99%

Section: Experimental Protocol 41 Protocolmentioning

confidence: 99%

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A Multi-sensor Approach for Biomimetic Control of a Robotic Prosthetic Hand

Ghataurah

Ferigo

Merhi

et al. 2017

Bioinformatics and Biomedical Engineering

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“…The coordinating variation of hip, knee, and ankle joint angles ( ) can be identified according to (2), indicating each reference gait pattern with corresponding muscle coactivation coefficient. Secondly, the generalized joint angle is estimated using the weighted average of all wavelets neural networks, which is calculated using…”

Section: Gait Generalization With Takagi-sugeno Fuzzy Inferencementioning

confidence: 99%

“…For its strong relationship with human motion pattern, sEMG has been taken as an ideal noninvasive control signal for exoskeletons [1], myoelectric prostheses [2], and biorobot [3] and moreover for the development of rehabilitation robots [4]. An important factor presenting in the sEMG-based biomechanical leg control, during the dynamic motion such as jumping and running, is the fact that multichannel sEMG signals contain mass data and vary greatly with time.…”

Section: Introductionmentioning

confidence: 99%

Generating Human-Like Velocity-Adapted Jumping Gait from sEMG Signals for Bionic Leg’s Control

Wang

Feng

et al. 2017

Journal of Sensors

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In the case of dynamic motion such as jumping, an important fact in sEMG (surface Electromyogram) signal based control on exoskeletons, myoelectric prostheses, and rehabilitation gait is that multichannel sEMG signals contain mass data and vary greatly with time, which makes it difficult to generate compliant gait. Inspired by the fact that muscle synergies leading to dimensionality reduction may simplify motor control and learning, this paper proposes a new approach to generate flexible gait based on muscle synergies extracted from sEMG signal. Two questions were discussed and solved, the first one concerning whether the same set of muscle synergies can explain the different phases of hopping movement with various velocities. The second one is about how to generate self-adapted gait with muscle synergies while alleviating model sensitivity to sEMG transient changes. From the experimental results, the proposed method shows good performance both in accuracy and in robustness for producing velocityadapted vertical jumping gait. The method discussed in this paper provides a valuable reference for the sEMG-based control of bionic robot leg to generate human-like dynamic gait.

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