Recent trends and challenges of surface electromyography in prosthetic applications

Yadav, Drishti; Veer, Karan

doi:10.1007/s13534-023-00281-z

Cited by 10 publications

(3 citation statements)

References 180 publications

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“…• sEMG signals are nonstationary and can vary due to physiological and environmental factors like fatigue, sweat, electrode displacement, and arm position. Such changes can impact the signals' amplitude, frequency, and morphology, thereby affecting the control system's accuracy and stability [146,147]. Therefore, adaptive and robust methods are needed to cope with these changes.…”

Section: Discussion Opportunities and Open Issuesmentioning

confidence: 99%

A Perspective on Prosthetic Hands Control: From the Brain to the Hand

Gentile,

Gruppioni

2023

Prosthesis

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The human hand is a complex and versatile organ that enables humans to interact with the environment, communicate, create, and use tools. The control of the hand by the brain is a crucial aspect of human cognition and behaviour, but also a challenging problem for both neuroscience and engineering. The aim of this study is to review the current state of the art in hand and grasp control from a neuroscientific perspective, focusing on the brain mechanisms that underlie sensory integration for hand control and the engineering implications for developing artificial hands that can mimic and interface with the human brain. The brain controls the hand by processing and integrating sensory information from vision, proprioception, and touch, using different neural pathways. The user’s intention can be obtained to control the artificial hand by using different interfaces, such as electromyography, electroneurography, and electroencephalography. This and other sensory information can be exploited by different learning mechanisms that can help the user adapt to changes in sensory inputs or outputs, such as reinforcement learning, motor adaptation, and internal models. This work summarizes the main findings and challenges of each aspect of hand and grasp control research and highlights the gaps and limitations of the current approaches. In the last part, some open questions and future directions for hand and grasp control research are suggested by emphasizing the need for a neuroscientific approach that can bridge the gap between the brain and the hand.

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Section: Discussion Opportunities and Open Issuesmentioning

confidence: 99%

A Perspective on Prosthetic Hands Control: From the Brain to the Hand

Gentile,

Gruppioni

2023

Prosthesis

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“…Changes in the fascicle length (i.e., muscle fascicle displacement) were determined by analyzing the collected brightness mode (B-mode) ultrasonographic image video during Pre-test, Post-test, Stretching 1, and Stretching 2. Feature points within an 8 mm by 8 mm square box of the fascicle image ( Fig 1E and 1F ) were identified based on the minimum eigenvalue algorithm [ 10 , 11 ] among potential feature extraction methods [ 12 – 14 ] to obtain the muscle fascicle displacement from the ultrasonographic image video, and their displacement was tracked by monitoring their positions throughout the frames. The Kanade–Lucas–Tomasi (KLT) feature tracking algorithm, executed in MATLAB (Mathworks, Ntick, MA, USA), was used to track the trajectory of each point.…”

Section: Methodsmentioning

confidence: 99%

Developing a device for simultaneously investigating pivoting neuromuscular control and muscle properties toward a multi-axis rehabilitation

Lee,

Kang,

Kim

et al. 2024

PLoS ONE

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Understanding the pivoting neuromuscular control of the lower limb and its associated muscle properties is critical for developing diagnostic and rehabilitation tools. However, to the best of our knowledge, a device that can evaluate these factors simultaneously remains lacking. To address this gap, a device that can investigate pivoting neuromuscular control and associated muscle properties was developed in this study. The proposed device consisted of a pivoting mechanism and height-adjustable chair with a brace interface. The device can control a footplate at various speeds to facilitate pivoting stretching and quantify neuromuscular control. Time-synchronized ultrasonographic images can be acquired simultaneously to quantify muscle properties during both active and passive pivoting movements. The muscle displacement, fascicle length/displacement, pennation angle, pivoting stiffness, and pivoting instability were investigated using the proposed device. Further, the feasibility of the device was demonstrated through a cross-sectional study with healthy subjects. The proposed device successfully quantified changes in muscle displacement during passive and active pivoting movements, pivoting stiffness during passive movements, and neuromuscular control during active movements. Therefore, the proposed device is expected to be used as a research and therapeutic tool for improving pivoting neuromuscular control and muscle functions and investigating the underlying mechanisms associated between muscle properties and joint movement in the transverse plane.

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“…Nevertheless, their performance, as that of all the EMG-based systems, is heavily dependent on a trade-off between the number of possible outputs, i.e., the number of possible classes, and the system robustness: the higher the number of classes, the lower the performance and robustness of the system. Specifically, the classification accuracy drops to 90-95% when the number of motion classes is increased by more than 10, compared to the initial accuracy of 99% achieved when only four classes are considered [10]. This effect can be observed as a limitation arising from both the utilized algorithms, as well as from the inherent difficulty faced by upper-limb amputees in generating precise and consistent contractions.…”

Section: Introductionmentioning

confidence: 93%

A Semiautonomous Control Strategy Based on Computer Vision for a Hand–Wrist Prosthesis

Cirelli,

Tamantini,

Cordella

et al. 2023

Robotics

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Alleviating the burden on amputees in terms of high-level control of their prosthetic devices is an open research challenge. EMG-based intention detection presents some limitations due to movement artifacts, fatigue, and stability. The integration of exteroceptive sensing can provide a valuable solution to overcome such limitations. In this paper, a novel semiautonomous control system (SCS) for wrist–hand prostheses using a computer vision system (CVS) is proposed and validated. The SCS integrates object detection, grasp selection, and wrist orientation estimation algorithms. By combining CVS with a simulated EMG-based intention detection module, the SCS guarantees reliable prosthesis control. Results show high accuracy in grasping and object classification (≥97%) at a fast frame analysis frequency (2.07 FPS). The SCS achieves an average angular estimation error ≤18° and stability ≤0.8° for the proposed application. Operative tests demonstrate the capabilities of the proposed approach to handle complex real-world scenarios and pave the way for future implementation on a real prosthetic device.

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Recent trends and challenges of surface electromyography in prosthetic applications

Cited by 10 publications

References 180 publications

A Perspective on Prosthetic Hands Control: From the Brain to the Hand

A Perspective on Prosthetic Hands Control: From the Brain to the Hand

Developing a device for simultaneously investigating pivoting neuromuscular control and muscle properties toward a multi-axis rehabilitation

A Semiautonomous Control Strategy Based on Computer Vision for a Hand–Wrist Prosthesis

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