Surface EMG Decomposition

Holobar, Aleš; Farina, Dario; Zazula, Damjan

doi:10.1002/9781119082934.ch07

Cited by 7 publications

(5 citation statements)

References 116 publications

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“…Relative movement between muscle fiber and electrode leads to non-stationarities in the recorded EMG, thereby distorting the shape of fiber action potentials over time as a function of fiber-to-electrode relative kinematics [123]. Despite challenges, recent work is supporting the possibility of HD-EMG decomposition during dynamic muscle contraction.…”

Section: Discussionmentioning

confidence: 99%

“…This will require extending current HD-EMG decomposition methods, now suitable for muscle isometric contractions only, to operate during both eccentric and concentric contraction types. Relative movement between muscle fiber and electrode leads to non-stationarities in the recorded EMG, thereby distorting the shape of fiber action potentials over time as a function of fiber-to-electrode relative kinematics [120]. Despite challenges, recent work is supporting the possibility of HD-EMG decomposition during dynamic muscle contraction.…”

Section: Discussionmentioning

confidence: 99%

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Closing the loop between wearable technology and human biology: A new paradigm for steering neuromuscular form and function

Sartori

Sawicki

2021

Prog. Biomed. Eng.

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Wearable technologies such as bionic limbs, robotic exoskeletons and neuromodulation devices have long been designed with the goal of enhancing human movement. However, current technologies have shown only modest results in healthy individuals and limited clinical impact. A central element hampering progress is that wearable technologies do not interact directly with tissues in the composite neuromuscular system. That is, current wearable systems do not take into account how biological targets (e.g., joints, tendons, muscles, nerves) react to mechanical or electrical stimuli, especially at extreme ends of the spatiotemporal scale (e.g., cell growth over months or years). Here, we outline a framework for 'closing-the-loop' between wearable technology and human biology. We envision a new class of wearable systems that will be classified as "steering devices" rather than "assistive devices" and outline the suggested research roadmap for the next 10-15 years. Wearable systems that steer, rather than assist, should be capable of delivering coordinated electro-mechanical stimuli to alter, in a controlled way, neuromuscular tissue form and function over time scales ranging from seconds (e.g., a movement cycle) to months (e.g., recovery stage following neuromuscular injuries) and beyond (e.g., across ageing stages). With an emphasis on spinal cord electrical stimulation and exosuits for the lower extremity, we explore developments in three key directions: (1) recording neuromuscular cellular activity from the intact moving human in vivo, (2) predicting tissue function and adaptation in response to electro-mechanical stimuli over time and (3) controlling tissue form and function with enough certainty to induce targeted, positive changes in the future. We discuss how this framework could restore, maintain or augment human movement and set the course for a new era in the development of symbiotic wearable devices. That is, devices designed to directly respond to biological cues to maintain integrity of underlying physiological systems over the lifespan.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Closing the loop between wearable technology and human biology: A new paradigm for steering neuromuscular form and function

Sartori

Sawicki

2021

Prog. Biomed. Eng.

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“…where N is the total number of labelled training samples, and N j is the number of samples belonging to class j. The weight matrix W can be routinely calculated by its closed-form least square solution in (4).…”

Section: Semg-based Hand Motion Recognitionmentioning

confidence: 99%

Electrotactile Feedback-based Muscle Fatigue Alleviation for Hand Manipulation

Zhou

et al. 2021

Int. J. Human. Robot.

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Tactile feedback is beneficial to improve the hand prosthesis performance, alleviate phantom pain, reduce muscle fatigue, etc. During the manipulation process, muscle fatigue not only causes discomfort to prosthesis users but also disturbs the sEMG-based motion recognition, which significantly deteriorates the prosthesis functional performance. Efforts have been made to explore appropriate signal processing algorithms which could be less influenced by muscle fatigue. However, few studies concern how to alleviate muscle fatigue directly. Thus, this study proposes a novel method to avoid excessive muscle fatigue based on electrotactile feedback. A potable electrotactile stimulator is developed with adjustable parameters, multiple channels and wireless communication. It is implemented in a virtual hand grasping platform driven by sEMG signals to investigate the impact of tactile feedback on muscle fatigue. Experiment results show a higher success rate of grasping with electrotactile feedback than that with no feedback. Moreover, compared with grasp in the no feedback condition, an observable decrease of sEMG intensity when grasping a heavy object with electrotactile feedback, despite a comparable performance on the light and medium objects in both feedback conditions. It indicates that tactile feedback helps to alleviate muscle fatigue caused by excessive muscle contraction, especially when large strength is needed.

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“…The number of muscle fibers housed in an MU is termed as innervation ratio, which in turn differs depending on the type and the function of muscle fibers. For instance, the innervation ratio of muscles that require a fine control, e.g., hand, ocular, and facial muscles, is low (close to 1:5), whereas the muscles requiring only a gross movement, e.g., back or leg antigravity muscles, are endowed with a high innervation ratio (1:2000) [32], [33].…”

Section: ) Muscle Structurementioning

confidence: 99%

Principal Component Analysis Applied to Surface Electromyography: A Comprehensive Review

et al. 2016

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Surface electromyography (sEMG) records muscle activities from the surface of muscles, which offers a wealth of information concerning muscle activation patterns in both research and clinical settings. A key principle underlying sEMG analyses is the decomposition of the signal into a number of motor unit action potentials (MUAPs) that capture most of the relevant features embedded in a low-dimensional space. Toward this, the principal component analysis (PCA) has extensively been sought after, whereby the original sEMG data are translated into low-dimensional MUAP components with a reduced level of redundancy. The objective of this paper is to disseminate the role of PCA in conjunction with the quantitative sEMG analyses. Following the preliminaries on the sEMG methodology and a statement of PCA algorithm, an exhaustive collection of PCA applications related to sEMG data is in order. Alongside the technical challenges associated with the PCA-based sEMG processing, the envisaged research trend is also discussed.INDEX TERMS Surface electromyography (sEMG), artificial neural network (ANN), principal component analysis (PCA), motor unit action potential (MUAP), flexions, self-organizing feature map (SOFM), support vector regression (SVR), myoelectric signal.

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Surface EMG Decomposition

Cited by 7 publications

References 116 publications

Closing the loop between wearable technology and human biology: A new paradigm for steering neuromuscular form and function

Closing the loop between wearable technology and human biology: A new paradigm for steering neuromuscular form and function

Electrotactile Feedback-based Muscle Fatigue Alleviation for Hand Manipulation

Principal Component Analysis Applied to Surface Electromyography: A Comprehensive Review

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