Surface Electromyography (sEMG)-based Thumb-tip Angle and Force Estimation Using Artificial Neural Network for Prosthetic Thumb

Sidek, Shahrul Na’im; Jalaludin, Nor Anija; Shamsudin, Abu Ubaidah

doi:10.1016/j.proeng.2012.07.225

Cited by 15 publications

(5 citation statements)

References 14 publications

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“…and y k = ln (L(k)) k = k 1 , k 2 , ..., k min (6) and n denotes the total number of k values for which the linear regression has been calculated.i.e., (2 ≤ n ≤ k max ).…”

Section: B Higuchi Fractal Dimensionmentioning

confidence: 99%

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Surface Electromyography Based Finger Flexion Recognition

Vijayan

John

Sudheer

2015

2015 IEEE International Conference on Computational Intelligence &Amp; Communication Technology

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Great effort is being taken since the last decade to make control interfaces for machines and robots less complicated and more natural, by taking signals directly from the body. This requires highly accurate signal interpretation. Researches have shown that electromyographic signals obtained from skeletal muscles can give very accurate information regarding limb movements, that can be used in human robot interface. In this paper, a novel approach toward classifying finger movements using surface Electromyography (sEMG) is discussed. Higuchi fractal dimensions and auto-regressive modeling are employed for extracting suitable features from a 6-channel EMG signal acquired indigenously using self-designed data acquisition hardware and classified using Multi Class Support Vector Machine. An average accuracy of 95.2% has been obtained.

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“…and y k = ln (L(k)) k = k 1 , k 2 , ..., k min (6) and n denotes the total number of k values for which the linear regression has been calculated.i.e., (2 ≤ n ≤ k max ).…”

Section: B Higuchi Fractal Dimensionmentioning

confidence: 99%

“…Gopura [5] developed exoskeleton robots for assisting physically weak individuals, using EMG signals. Sidek SN [6] investigated the relation between EMG signals and thumb-tip forces for the development of a prosthetic thumb. Mulas M[7] developed an EMG controlled exoskeleton for rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

Surface Electromyography Based Finger Flexion Recognition

Vijayan

John

Sudheer

2015

2015 IEEE International Conference on Computational Intelligence &Amp; Communication Technology

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“…At the same time, dynamic technological development is being observed in sEMG equipment, providing more and more new opportunities in the application of this technique in different fields of medicine, such as rehabilitation. For example, the extraction of time-domain features from the sEMG signals uses Gradient Boosted Regression Tree ensembles to estimate the kinematics of the wrist and fingers, or for new techniques to identify low level hand movement by classifying a single channel sEMG [1,6,7,8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

The usefulness of surface electromyography in rehabilitation and physiotherapy: systematic review

Radecka

Lubkowska

2020

Pomeranian Journal of Life Sciences

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Introduction: Technological developments in surface electromyography (sEMG) equipment are providing more and more new opportunities in the application of this technique in different fields, not just in clinical medicine but also in physiotherapy.The purpose of this study is an educational review that provides examples of the clinical use of sEMG in rehabilitation and physiotherapy.Materials and methods: Studies were searched for on the PubMed and ScienceDirect databases using the following descriptors: “sEMG rehabilitation”, “sEMG physiotherapy”, “surface electromyography physiotherapy”, “surface electromyography rehabilitation”, resulting in 28,040 citations in total. After reviewing for inclusion criteria – methodological quality assessment using the Physiotherapy Evidence Database (PEDro) scale and consistency with the theme of systematic review – 28,023 studies were removed from the analysis.Results: The qualified 14 works were divided into 4 categories depending on the sEMG application area: urogenital system dysfunction (322 patients, PEDro scale average rating of 6.33), central nervous system damage (120 patients, PEDro scale average rating of 6.6), lower back pain (181 patients, PEDro scale average rating of 5.33) and musculoskeletal disorders (244 patients, PEDro scale average rating of 6.66).Conclusions: The most recent examples of the use of sEMG in intervention studies in rehabilitation and physiotherapy in the last 5 years concerned neurology, urology, gynaecology, paediatrics, pulmonary medicine and orthopaedics.

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“…En los últimos años, se han visto numerosos avances en lo referente a prótesis robóticas altamente similares en apariencia y con prestaciones cada vez más cercanas a las de una extremidad real [1], [2][3]. Actualmente, en varios lugares del mundo, se realizan investigaciones que proporcionan gran cantidad de desarrollos con el fin de mejorar las prótesis de extremidad superior, mediante el uso de materiales inteligentes, sensores, actuadores y aplicaciones para la adquisición, filtrado y manejo de señales mioeléctricas residuales, normalmente de los músculos del muñón, para el control efectivo de una prótesis de mano [4][5][6].…”

Section: Introductionunclassified

Adquisición de señales SEMG con electrodos secos para el control de movimiento de dedos en una prótesis robótica fabricada en una impresora 3D

Castillo

Flores

Gómez

et al. 2019

Ingeniare. Rev. chil. ing.

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Adquisición de señales SEMG con electrodos secos para el control de movimiento de dedos en una prótesis robótica fabricada en una impresora 3D RESUMEN Este artículo presenta la comparación de dos tipos de electrodos, húmedos del tipo Ag/AgCl y secos de acero Inoxidable, y el desarrollo de la adquisición y filtrado de señales SEMG (Señales electromiográficas superficiales) para el control de una prótesis robótica, fabricada en una impresora 3D, para personas con amputación del miembro superior. El trabajo muestra que es factible la utilización de electrodos secos de acero inoxidable para la correcta obtención de las señales del músculo y el control de la prótesis. Además, se muestra la posibilidad de realizar el movimiento de dedos en forma secuencial mediante el uso de un solo sensor (conformado por tres electrodos en un solo módulo y un amplificador de señales) utilizando un filtro pasa bajo digital. Los resultados de esta investigación servirán para el desarrollo de prótesis más avanzadas y con componentes comunes para personas con discapacidad, y así, estas personas puedan tener una mejor calidad de vida.Palabras clave: Electromiografía, prótesis de mano, señales SEMG, control de dedos. ABSTRACTThis paper presents the comparison of two types of electrodes, wet of type Ag/AgCl and dry of stainless steel, and the development of the acquisition and filtering of SEMG signals (surface Electromyography signals) for the control of robotic prosthetic printed in 3D for people with amputation of the upper limb. The work shows that the use of dry electrodes of stainless steel for the correct acquisition of muscle signals and the control of the prosthetic is viable. In addition, the possibility of performing the sequential movement of fingers using a single sensor (made up with three electrodes in only one module and a signal amplifier) using a low-pass filter is also shown. The results of this research will be useful for the development of more advanced and lower cost prosthetics for people with disabilities, and thus, these people can have a better quality of life in the future.

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Surface Electromyography (sEMG)-based Thumb-tip Angle and Force Estimation Using Artificial Neural Network for Prosthetic Thumb

Cited by 15 publications

References 14 publications

Surface Electromyography Based Finger Flexion Recognition

Surface Electromyography Based Finger Flexion Recognition

The usefulness of surface electromyography in rehabilitation and physiotherapy: systematic review

Adquisición de señales SEMG con electrodos secos para el control de movimiento de dedos en una prótesis robótica fabricada en una impresora 3D

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