Arm Orthosis/Prosthesis Movement Control Based on Surface EMG Signal Extraction

Suberbiola, Aaron; Zulueta, Ekaitz; López-Guede, José Manuel; Etxeberria‐Agiriano, Ismael; Graña, Manuel

doi:10.1142/s0129065715500094

Cited by 20 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…28 The resulting EMG signals are usually processed as global interference signals for extracting their amplitude as a crude estimate of nerve activity. [29][30][31][32] However, recently, it has been proposed that the EMG signals from reinnervated muscles can be decomposed so that the efferent neural information of the innervating nerves can be extracted directly. 2,27 In this way, a neural interface is established which provides the same information as if the nerve would be directly interfaced with neural intrafascicular electrodes.…”

mentioning

confidence: 99%

A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings

Karimimehr

Marateb

Muceli

et al. 2017

Int. J. Neur. Syst.

View full text Add to dashboard Cite

The neural command from motor neurons to muscles -sometimes referred to as the neural drive to muscle -can be identified by decomposition of electromyographic (EMG) paper proposes for the first time an innovative method for fully automatic and real-time intramuscular EMG (iEMG) decomposition. The method is based on online single-pass density-based clustering and adaptive classification of bivariate features, using the concept of potential measure. No attempt was made to resolve superimposed motor unit action potentials. The proposed algorithm was validated on sets of simulated and experimental iEMG signals. Signals were recorded from the biceps femoris long-head, vastus medialis and lateralis and tibialis anterior muscles during low-to-moderate isometric constant-force and linearly-varying force contractions. The average number of missed, duplicated and erroneous clusters for the examined signals was 0.5 ± 0.8, 1.2 ± 1.0, and 1.0 ± 0.8, respectively. The average decomposition accuracy (defined similar to signal detection theory but without using True Negatives in the denominator) and coefficient of determination (variance accounted for) for the cumulative discharge rate estimation were 70 ± 9%, and 94 ± 5%, respectively. The time cost for processing each 200 ms iEMG interval was 43 ± 16 (21-97) ms. However, computational time generally increases over time as a function of frames/signal epochs. Meanwhile, the incremental accuracy defined as the accuracy of real-time analysis of each signal epoch, was 74 ± 18% for epochs recorded after initial one second. The proposed algorithm is thus a promising new tool for neural decoding in the next-generation of prosthetic control.

show abstract

mentioning

confidence: 99%

A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings

Karimimehr

Marateb

Muceli

et al. 2017

Int. J. Neur. Syst.

View full text Add to dashboard Cite

show abstract

“…Knowledge from multiple disciplines including anatomy, physiology, electronics, mechanical design and software must be interwoven to simulate the abilities of a human hand [4]. Due to the complexity of the movements of the hand, EMG electrodes have been employed by many researchers to obtain biopotential signals from the forearm muscles to control hand prostheses [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Flexible Surface Electrodes Targeting Biopotential Signals from Forearm Muscles for Control of Prosthetic Hands: Part 1 – Characterisation of sEMG Electrodes

O'Brien

Searle

Alıcı

2019

2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

View full text Add to dashboard Cite

This study is Part 1 of two studies which investigate the use of various flexible surface sensors as an alternative to the gold standard Ag/AgCl surface electromyography (sEMG) electrodes in identifying movement intention from a user during common hand gestures. Three conductive textiles, two commercial conductive elastomers and one E-skin elastomer produced on site were tested as biopotential electrodes to establish the efficacy of each in gathering movement intention from the human brain at the level of the muscle. Testing was performed in vivo on two participants across three hand gestures, with results demonstrating that sEMG electrodes made from a commercially sourced conductive fabric can outperform the traditional Ag/AgCl sEMG electrodes, obtaining substantially larger peak and RMS measurements. Given the disadvantages of Ag/AgCl electrodes over long usage periods, namely their tendency to dry out and significant skin preparation, resulting in variable impedances and skin irritation respectively, the incorporation of flexible surface EMG electrodes in hand prosthetic control systems would increase functionality of the prosthetic devices, consequently increasing the quality of life of prosthetic hand users. Disciplines Disciplines Engineering | Science and Technology Studies Publication Details Publication Details O'Brien, S., Searle, T. & Alici, G. (2019). Flexible surface electrodes targeting biopotential signals from forearm muscles for control of prosthetic hands: Part 1-Characterisation of semg electrodes. IEEE/ ASME International Conference on Advanced Intelligent Mechatronics, AIM (pp. 1019-1024). United States: IEEE.

show abstract

“…In recent years, research has focused on representation, analysis, and processing of measured nonstationary signal . TF representation methods such as the wavelet transform (WT) provide a powerful tool for the analysis of time series signals .…”

Section: Introductionmentioning

confidence: 99%

“…[13,14] In recent years, research has focused on representation, analysis, and processing of measured nonstationary signal. [15][16][17] TF representation methods such as the wavelet transform (WT) provide a powerful tool for the analysis of time series signals. [18][19][20][21] A number of researchers have used the WT to identify structural modal parameters [22][23][24][25] and develop integrated structural system identification techniques.…”

Section: Introductionmentioning

confidence: 99%

New method for modal identification of super high-rise building structures using discretized synchrosqueezed wavelet and Hilbert transforms

Park

Adeli

2016

Struct Design Tall Spec Build

133

106

View full text Add to dashboard Cite

Summary Measured signals obtained by sensors during dynamic events such as earthquake, wind, and wave contain nonlinear, nonstationary, and noisy properties. In this paper, a new approach is presented for modal parameter identification of structures particularly suitable for very large real‐life structures such as super high‐rise building structures based on the integration of discretized synchrosqueezed wavelet transform, the Hilbert transform, and the linear least‐square fit. Its effectiveness is demonstrated first by application to a two‐dimensional frames from the literature, and then to the 123‐story Lotte World Tower (LWT) under construction in Seoul, Korea. The LWT measurements are very low‐amplitude ambient vibrations. Extracting the natural frequencies and damping ratios from such low‐amplitude signals are known to be very challenging. Further, the new methodology was compared with the empirical mode decomposition. It is demonstrated that the new method is capable of extracting both natural frequencies and damping rations from low‐amplitude signals effectively and with a higher accuracy compared with the empirical mode decomposition approach. The results of this research indicate a super high‐rise building like LWT has a damping ratio in the range 0.7–3.4%. The new method is quite promising for practical implementations of health monitoring of large real‐life structures.

show abstract

Arm Orthosis/Prosthesis Movement Control Based on Surface EMG Signal Extraction

Cited by 20 publications

References 25 publications

A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings

A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings

Flexible Surface Electrodes Targeting Biopotential Signals from Forearm Muscles for Control of Prosthetic Hands: Part 1 – Characterisation of sEMG Electrodes

New method for modal identification of super high-rise building structures using discretized synchrosqueezed wavelet and Hilbert transforms

Contact Info

Product

Resources

About