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1.
Motor unit characteristics after targeted muscle reinnervation
Tamás Kapelner, Ning Jiang, Aleš Holobar, Ivan Vujaklija, Aidan Roche, Dario Farina, Oskar Aszmann, 2016, original scientific article

Abstract: Targeted muscle reinnervation (TMR) is a surgical procedure used to redirect nerves originally controlling muscles of the amputated limb into remaining muscles above the amputation, to treat phantom limb pain and facilitate prosthetic control. While this procedure effectively establishes robust prosthetic control, there is little knowledge on the behavior and characteristics of the reinnervated motor units. In this study we compared the m. pectoralis of five TMR patients to nine able-bodied controls with respect to motor unit action potential (MUAP) characteristics. We recorded and decomposed high-density surface EMG signals into individual spike trains of motor unit action potentials. In the TMR patients the MUAP surface area normalized to the electrode grid surface (0.25 ± 0.17 and 0.81 ± 0.46, p < 0.001) and the MUAP duration (10.92 ± 3.89 ms and 14.03 ± 3.91 ms, p < 0.01) were smaller for the TMR group than for the controls. The mean MUAP amplitude (0.19 ± 0.11 mV and 0.14 ± 0.06 mV, p = 0.07) was not significantly different between the two groups. Finally, we observed that MUAP surface representation in TMR generally overlapped, and the surface occupied by motor units corresponding to only one motor task was on average smaller than 12% of the electrode surface. These results suggest that smaller MUAP surface areas in TMR patients do not necessarily facilitate prosthetic control due to a high degree of overlap between these areas, and a neural information—based control could lead to improved performance. Based on the results we also infer that the size of the motor units after reinnervation is influenced by the size of the innervating motor neuron.
Keywords: target muscle reinnervation, motor unit, controlling muscles
Published in DKUM: 19.06.2017; Views: 1065; Downloads: 359
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2.
UVEDBA METODOLOGIJE DMAIC V PODJETJU ISKRA MEHANIZMI D.O.O.
Borut Kavčič, 2016, bachelor thesis/paper

Abstract: Vsaka proizvodna enota, ki danes želi uresničevati zahteve po cenovni konkurenčnosti in biti hkrati kapitalsko donosna, mora poskrbeti za nenehno odpravljanje vsakršnih izgub v proizvodnji. V diplomskem delu je obravnavano reševanje problema povečanega izmeta v realnem okolju proizvodne motorjev DMU. Izmet je lahko posledica različnih vzrokov, zato so v teoretičnem delu naloge predstavljene ugotovitve s področja učinkovitosti proizvodnih procesov in vitke proizvodnje. Opisan je tudi pristop šest sigma in metodologija DMAIC, s katero smo problem reševali. Diplomsko delo temelji na projektnem pristopu uvajanja metodologije DMAIC v proizvodnjo z namenom zmanjšanja izmeta. V praktičnem delu naloge so predstavljeni posamezni koraki te metodologije, ki so od definiranja problema, opisa začetnega stanja ter izvedenih meritev in analiz pripeljali projektni tim do rešitev, implementacije le teh in obenem zagotovili ohranjanje pridobljenih rezultatov implementiranih izboljšav. Projektni pristop in metodologija DMAIC sta se izkazala kot učinkovit način za odpravo določenih izgub v proizvodnji in dvig delovne kulture zaposlenih, ki je temelj nadaljnjih nenehnih izboljšav.
Keywords: prenova procesov, metodologija DMAIC, učinkovito vzdrževanje, DMU (Drum Motor Unit), mali projekt
Published in DKUM: 04.10.2016; Views: 1458; Downloads: 47
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3.
Real-time motor unit identification from high-density surface EMG
Vojko Glaser, Aleš Holobar, Damjan Zazula, 2013, original scientific article

Abstract: This study addresses online decomposition of high-density surface electromyograms (EMG) in real-time. The proposed method is based on previouslypublished Convolution Kernel Compensation (CKC) technique and sharesthe same decomposition paradigm, i.e. compensation of motor unit action potentials and direct identification of motor unit (MU) discharges. In contrast to previously published version of CKC, which operates in batch mode and requires ~ 10 s of EMG signal, the real-time implementation begins with batch processing of ~ 3 s of the EMG signal in the initialization stage and continues on with iterative updating of the estimators of MU discharges as blocks of new EMG samples become available. Its detailed comparison to previously validated batch version of CKC and asymptotically Bayesian optimal Linear Minimum Mean Square Error (LMMSE) estimator demonstrates high agreementin identified MU discharges among all three techniques. In the case of synthetic surface EMG with 20 dB signal-to-noise ratio, MU discharges were identified with average sensitivity of 98 %. In the case of experimental EMG, real-time CKC fully converged after initial 5 s of EMG recordings and real-time and batch CKC agreed on 90 % of MU discharges, on average. The real-time CKC identified slightly fewer MUs than its batch version (experimental EMG, 4 MUs versus 5 MUs identified by batch CKC, on average), but required only 0.6 s of processing time on regular personal computer for each second of multichannel surface EMG.
Keywords: discharge pattern, high-density EMG, surface EMG, motor unit, real time decomposition
Published in DKUM: 25.05.2015; Views: 1316; Downloads: 0

4.
5.
Noninvasive, accurate assessment of the behavior of representative populations of motor units in targeted reinnervated muscles
Dario Farina, Hubertus Rehbaum, Aleš Holobar, Ivan Vujaklija, Ning Jiang, Christian Hofer, Stefan Salminger, Hans-Willem van Vliet, Oskar Aszmann, 2014, original scientific article

Abstract: Targeted muscle reinnervation (TMR) redirects nerves that have lost their target, due to amputation, to remaining muscles in the region of the stump with the intent of establishing intuitive myosignals to control a complex prosthetic device. In order to directly recover the neural code underlying an attempted limb movement, in this paper, we present the decomposition of high-density surface electromyographic (EMG) signals detected from three TMR patients into the individual motor unit spike trains. The aim was to prove, for the first time, the feasibility of decoding the neural drive that would reach muscles of the missing limb in TMR patients, to show the accuracy of the decoding, and to demonstrate the representativeness of the pool of extracted motor units. Six to seven flexible EMG electrode grids of 64 electrodes each were mounted over the reinnervated muscles of each patient, resulting in up to 448 EMG signals. The subjects were asked to attempt elbow extension and flexion, hand open and close, wrist extension and flexion, wrist pronation and supination, of their missing limb. The EMG signals were decomposed using the Convolution Kernel Compensation technique and the decomposition accuracy was evaluated with a signal-based index of accuracy, called pulse-to-noise ratio (PNR). The results showed that the spike trains of 3 to 27 motor units could be identified for each task, with a sensitivity of the decomposition > 90%, as revealed by PNR. The motor unit discharge rates were within physiological values of normally innervated muscles. Moreover, the detected motor units showed a high degree of common drive so that the set of extracted units per task was representative of the behavior of the population of active units. The results open a path for a new generation of human-machine interfaces in which the control signals are extracted from noninvasive recordings and the obtained neural information is based directly on the spike trains of motor neurons.
Keywords: electromyographic, EMG, decomposition, high-density EMG, motor neuron, motor unit, myoelectronic control, neural drive to muscle, target muscle reinervation, TMR
Published in DKUM: 25.05.2015; Views: 1239; Downloads: 0

6.
The extraction of neural information from the surface EMG for the control of upper-limb prostheses : emerging avenues and challenges
Dario Farina, Ning Jiang, Hubertus Rehbaum, Aleš Holobar, Bernhard Graimann, Hans Dietl, Oskar Aszmann, 2014, original scientific article

Abstract: Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e., the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time ( 200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.
Keywords: neural drive to muscle, high-density EMG, motor neuron, motor unit, myoelectronic control, pattern recognition, regression
Published in DKUM: 25.05.2015; Views: 1100; Downloads: 0

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