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      Spatial localization of electromyographic amplitude distributions associated to the activation of dorsal forearm muscles

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          Abstract

          In this study we investigated whether the spatial distribution of surface electromyographic (EMG) amplitude can be used to describe the activation of muscle portions with different biomechanical actions. Ten healthy subjects performed isometric contractions aimed to selectively activate a number of forearm muscles or muscle subportions. Monopolar electromyographic signals were collected with an electrode grid of 128 electrodes placed on the proximal, dorsal portion of the forearm. The monopolar EMG amplitude [root mean square (RMS) value] distribution was calculated for each contraction, and high-amplitude channels were identified through an automatic procedure; the position of the EMG source was estimated with the barycenter of these channels. Each of the contractions tested was associated to a specific EMG amplitude distribution, whose location in space was consistent with the expected anatomical position of the main agonist muscle (or subportion). The position of each source was significantly different from the others in at least one direction (ANOVA; transversally to the forearm: P < 0.01, F = 125.92; longitudinally: P < 0.01, F = 35.83). With such an approach, we could distinguish the spatial position of EMG distributions related to the activation of contiguous muscles [e.g., extensor carpi ulnaris (ECU) and extensor digitorum communis (EDC)], different heads of the same muscle (i.e., extensor carpi radialis (ECR) brevis and longus) and different functional compartments (i.e., EDC, middle, and ring fingers). These findings are discussed in terms of how forces along a given direction can be produced by recruiting population of motor units clustered not only in specific muscles, but also in muscle sub-portions. In addition, this study supports the use of high-density EMG systems to characterize the activation of muscle subportions with different biomechanical actions.

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          Most cited references28

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          Tennis elbow. The surgical treatment of lateral epicondylitis.

          Of the 1,213 clinical cases of lateral tennis elbow seen during the time period from December 19, 1971, to October 31, 1977, eighty-eight elbows in eighty-two patients had operative treatment. The lesion that was consistently identified at surgery was immature fibroblastic and vascular infiltration of the origin of the extensor carpi radialis brevis. A specific surgical technique was employed, including exposure of the extensor carpi radialis brevis, excision of the identified lesion, and repair. The results at follow-up were rated as excellent in sixty-six elbows, good in nine, fair in eleven, and failed in two. There was an over-all improvement rate of 97.7 per cent, and 85.2 per cent of the patients returned to full activity including rigorous sports.
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            Simultaneous and proportional estimation of hand kinematics from EMG during mirrored movements at multiple degrees-of-freedom.

            This paper proposes and tests on able-bodied subjects a control strategy that can be practically applied in unilateral transradial amputees for simultaneous and proportional control of multiple degrees-of-freedom (DOFs). We used artificial neural networks to estimate kinematics of the complex wrist/hand from high-density surface electromyography (EMG) signals of the contralateral limb during mirrored bilateral movements in free space. The movements tested involved the concurrent activation of wrist flexion/extension, radial/ulnar deviation, forearm pronation/supination, and hand closing. The accuracy in estimation was in the range 79%-88% (r(2) index) for the four DOFs in six able-bodied subjects. Moreover, the estimation of the pronation/supination angle (wrist rotation) was influenced by the reduction in the number of EMG channels used for the estimation to a greater extent than the other DOFs. In conclusion, the proposed method and set-up provide a viable means for proportional and simultaneous control of multiple DOFs for hand prostheses.
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              A comparison of the effects of electrode implantation and targeting on pattern classification accuracy for prosthesis control.

              The use of surface versus intramuscular electrodes as well as the effect of electrode targeting on pattern-recognition-based multifunctional prosthesis control was explored. Surface electrodes are touted for their ability to record activity from relatively large portions of muscle tissue. Intramuscular electromyograms (EMGs) can provide focal recordings from deep muscles of the forearm and independent signals relatively free of crosstalk. However, little work has been done to compare the two. Additionally, while previous investigations have either targeted electrodes to specific muscles or used untargeted (symmetric) electrode arrays, no work has compared these approaches to determine if one is superior. The classification accuracies of pattern-recognition-based classifiers utilizing surface and intramuscular as well as targeted and untargeted electrodes were compared across 11 subjects. A repeated-measures analysis of variance revealed that when only EMG amplitude information was used from all available EMG channels, the targeted surface, targeted intramuscular, and untargeted surface electrodes produced similar classification accuracies while the untargeted intramuscular electrodes produced significantly lower accuracies. However, no statistical differences were observed between any of the electrode conditions when additional features were extracted from the EMG signal. It was concluded that the choice of electrode should be driven by clinical factors, such as signal robustness/stability, cost, etc., instead of by classification accuracy.

                Author and article information

                Journal
                Front Physiol
                Front Physiol
                Front. Physiol.
                Frontiers in Physiology
                Frontiers Media S.A.
                1664-042X
                13 December 2013
                2013
                : 4
                : 367
                Affiliations
                Laboratory for Engineering of the Neuromuscular System (LISiN), Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino Torino, Italy
                Author notes

                Edited by: Taian M. M. Vieira, Universidade Federal do Rio de Janeiro, Brazil

                Reviewed by: Kohei Watanabe, Chukyo University, Japan; Silvia Muceli, Bernstein Center for Computational Neuroscience, Germany

                *Correspondence: Alessio Gallina, Laboratory for Engineering of the Neuromuscular System (LISiN), Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy e-mail: alessio.gallina@ 123456delen.polito.it

                This article was submitted to Integrative Physiology, a section of the journal Frontiers in Physiology.

                Article
                10.3389/fphys.2013.00367
                3861694
                24379788
                d4f7a810-63e6-4859-b167-e5195b1d8e42
                Copyright © 2013 Gallina and Botter.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 09 September 2013
                : 25 November 2013
                Page count
                Figures: 5, Tables: 1, Equations: 0, References: 29, Pages: 8, Words: 6797
                Categories
                Physiology
                Original Research Article

                Anatomy & Physiology
                forearm,finger,muscle compartmentalization,wrist,electromyography,high-density surface emg

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