Electromyogram signal analysis and movement recognition based on wavelet packet transform

Yang, Peng; Zu, Linan; Xu, Xin

doi:10.1109/icinfa.2009.5205151

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Wavelet packet transform [116][117][118], discrete wavelet transform [24,119] Time and frequency resolution [102], transient and static representation [116] Abstract features, high-dimensional outputs, many design parameters…”

Section: Timefrequencymentioning

confidence: 99%

“…Time-frequency domain. Time-frequency features represent transient as well as steady state patterns from dynamic contractions [24,102,[116][117][118][119][158][159][160][161]. Multiresolution analysis with wavelets transforms signals to a high-dimensional sparse domain, revealing characteristics that most other extraction techniques miss [3].…”

Section: Synergy Featuresmentioning

confidence: 99%

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The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

Ison

Artemiadis²

2014

J. Neural Eng.

164

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Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.

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Section: Timefrequencymentioning

confidence: 99%

Section: Synergy Featuresmentioning

confidence: 99%

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

Ison

Artemiadis²

2014

J. Neural Eng.

164

View full text Add to dashboard Cite

show abstract

“…Small electrical currents is generated by muscle fibres prior to the production of the muscle force [8]. Nowadays, EMG is used for biofeedback or ergonomic assessment, laboratories research including motor control, neuromuscular physiology, biomechanics and etc [1].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Surface Electromyography for On-Off Control

Seng

Sia

Wong

et al. 2013

AMR

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Myogram on-and-off controller is important for improving or assisting the elderly people. One of the most important aspects of the controller development is to determine the on and off time with respect to the body movement. In this project, high accuracy signal filtering, high gain amplifier, signal converter, microcontroller and electrodes are used for circuit simulation and development to obtain muscle signal (Electromyogram). Precision rectifier is used to solve the ordinary semiconductor problem to avoid signal block. To ensurethe user-friendliness in the development of this device, non-invasive electrodes are used in this project instead of invasive electrodes.

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“…EMG features can be extracted in either time, frequency, or time frequency domains. An example of a time-frequency extraction method is the wavelet packet transform[74,75]. After feature extraction, dimensionality reduction can decrease the number of features processed by classifiers to supply only the most relevant information.…”

mentioning

confidence: 99%

Electromyography-based control of lower limb prostheses: a systematic review

Ahkami¹,

Ahmed²,

Thesleff³

et al. 2022

Preprint

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Most amputations occur in lower limbs and despite improvements in prosthetic technology, no commercially available prosthetic leg uses electromyography (EMG) information as an input for control. Efforts to integrate <a>EMG </a>signals as part of the control strategy have increased in the last decade. In this review, we summarize the research in the field of lower limb prosthetic control using EMG. Four different online databases were searched from July-September 2020: Web of Science, Scopus, PubMed, and Science Direct. We included articles that reported systems for controlling a prosthetic leg (with an ankle and/or knee actuator) by decoding gait intent using EMG signals alone or in combination with other sensors. A total of 1,327 papers were initially assessed and 117 were finally included in this review. The literature showed that despite the burgeoning interest in research, controlling a leg prosthesis using EMG signals remains challenging. Specifically, regarding EMG signal quality and stability, electrode placement, prosthetic hardware, and control algorithms, all of which need to be more robust for everyday use. In the studies that were investigated large variations were found across studies between the different control methodologies, type of research participant, recording protocols, assessments, and prosthetic hardware.

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Electromyogram signal analysis and movement recognition based on wavelet packet transform

Cited by 6 publications

References 6 publications

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

Analysis of Surface Electromyography for On-Off Control

Electromyography-based control of lower limb prostheses: a systematic review

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