Farina, Dario

[["dc.bibliographiccitation.artnumber","eaau2956"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Science Advances"],["dc.bibliographiccitation.lastpage","eaau2956"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Bergmeister, Konstantin D."],["dc.contributor.author","Aman, Martin"],["dc.contributor.author","Muceli, Silvia"],["dc.contributor.author","Vujaklija, Ivan"],["dc.contributor.author","Manzano-Szalai, Krisztina"],["dc.contributor.author","Unger, Ewald"],["dc.contributor.author","Byrne, Ruth A."],["dc.contributor.author","Scheinecker, Clemens"],["dc.contributor.author","Riedl, Otto"],["dc.contributor.author","Salminger, Stefan"],["dc.contributor.author","Frommlet, Florian"],["dc.contributor.author","Borschel, Gregory H."],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Aszmann, Oskar C."],["dc.date.accessioned","2019-07-09T11:50:25Z"],["dc.date.available","2019-07-09T11:50:25Z"],["dc.date.issued","2019"],["dc.description.abstract","Selective nerve transfers surgically rewire motor neurons and are used in extremity reconstruction to restore muscle function or to facilitate intuitive prosthetic control. We investigated the neurophysiological effects of rewiring motor axons originating from spinal motor neuron pools into target muscles with lower innervation ratio in a rat model. Following reinnervation, the target muscle's force regenerated almost completely, with the motor unit population increasing to 116% in functional and 172% in histological assessments with subsequently smaller muscle units. Muscle fiber type populations transformed into the donor nerve's original muscles. We thus demonstrate that axons of alternative spinal origin can hyper-reinnervate target muscles without loss of muscle force regeneration, but with a donor-specific shift in muscle fiber type. These results explain the excellent clinical outcomes following nerve transfers in neuromuscular reconstruction. They indicate that reinnervated muscles can provide an accurate bioscreen to display neural information of lost body parts for high-fidelity prosthetic control."],["dc.identifier.doi","10.1126/sciadv.aau2956"],["dc.identifier.pmid","30613770"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15938"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59771"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/267888/EU//DEMOVE"],["dc.relation.issn","2375-2548"],["dc.rights","CC BY-NC 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc/4.0"],["dc.subject.ddc","610"],["dc.title","Peripheral nerve transfers change target muscle structure and function"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","25"],["dc.bibliographiccitation.journal","Journal of NeuroEngineering and Rehabilitation"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Lorrain, Thomas"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T08:56:14Z"],["dc.date.available","2018-11-07T08:56:14Z"],["dc.date.issued","2011"],["dc.description.abstract","Background: For high usability, myo-controlled devices require robust classification schemes during dynamic contractions. Therefore, this study investigates the impact of the training data set in the performance of several pattern recognition algorithms during dynamic contractions. Methods: A 9 class experiment was designed involving both static and dynamic situations. The performance of various feature extraction methods and classifiers was evaluated in terms of classification accuracy. Results: It is shown that, combined with a threshold to detect the onset of the contraction, current pattern recognition algorithms used on static conditions provide relatively high classification accuracy also on dynamic situations. Moreover, the performance of the pattern recognition algorithms tested significantly improved by optimizing the choice of the training set. Finally, the results also showed that rather simple approaches for classification of time domain features provide results comparable to more complex classification methods of wavelet features. Conclusions: Non-stationary surface EMG signals recorded during dynamic contractions can be accurately classified for the control of multi-function prostheses."],["dc.identifier.doi","10.1186/1743-0003-8-25"],["dc.identifier.isi","000291589200001"],["dc.identifier.pmid","21554700"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6371"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/23090"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1743-0003"],["dc.rights","CC BY 2.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.0"],["dc.title","Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","335"],["dc.bibliographiccitation.journal","Frontiers in Human Neuroscience"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Oliveira, Anderson Souza Castelo"],["dc.contributor.author","Gizzi, Leonardo"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Kersting, Uwe Gustav"],["dc.date.accessioned","2018-11-07T09:39:59Z"],["dc.date.available","2018-11-07T09:39:59Z"],["dc.date.issued","2014"],["dc.description.abstract","Locomotion can be investigated by factorization of electromyographic (EMG) signals, e.g., with non-negative matrix factorization (NMF). This approach is a convenient concise representation of muscle activities as distributed in motor modules, activated in specific gait phases. For applying NMF, the EMG signals are analyzed either as single trials, or as averaged EMG, or as concatenated EMG (data structure). The aim of this study is to investigate the influence of the data structure on the extracted motor modules. Twelve healthy men walked at their preferred speed on a treadmill while surface EMG signals were recorded for 60 s from 10 lower limb muscles. Motor modules representing relative weightings of synergistic muscle activations were extracted by NMF from 40 step cycles separately (EMG(SNG)), from averaging 2, 3, 5, 10, 20, and 40 consecutive cycles (EMG(AVR)), and from the concatenation of the same sets of consecutive cycles (EMG(CNC)). Five motor modules were sufficient to reconstruct the original EMG datasets (reconstruction quality >90%), regardless of the type of data structure used. However, EMG(CNC) was associated with a slightly reduced reconstruction quality with respect to EMG(AVR). Most motor modules were similar when extracted from different data structures (similarity >0.85). However, the quality of the reconstructed 40-step EMG(CNC) datasets when using the muscle weightings from EMG(AVR) was low (reconstruction quality similar to 40%). On the other hand, the use of weightings from EMG(CNC) for reconstructing this long period of locomotion provided higher quality, especially using 20 concatenated steps (reconstruction quality similar to 80%). Although EMG(SNG) and EMG(AVR) showed a higher reconstruction quality for short signal intervals, these data structures did not account for step-to-step variability. The results of this study provide practical guidelines on the methodological aspects of synergistic muscle activation extraction from EMG during locomotion."],["dc.identifier.doi","10.3389/fnhum.2014.00335"],["dc.identifier.isi","000339497900001"],["dc.identifier.pmid","24904375"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11791"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33414"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1662-5161"],["dc.relation.issn","1662-5161"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Motor modules of human locomotion: influence of EMG averaging, concatenation, and number of step cycles"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","55"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of NeuroEngineering and Rehabilitation"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Dosen, Strahinja"],["dc.contributor.author","Markovic, Marko"],["dc.contributor.author","Somer, Kelef"],["dc.contributor.author","Graimann, Bernhard"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2019-07-09T11:41:46Z"],["dc.date.available","2019-07-09T11:41:46Z"],["dc.date.issued","2015"],["dc.description.abstract","Background Active hand prostheses controlled using electromyography (EMG) signals have been used for decades to restore the grasping function, lost after an amputation. Although myocontrol is a simple and intuitive interface, it is also imprecise due to the stochastic nature of the EMG recorded using surface electrodes. Furthermore, the sensory feedback from the prosthesis to the user is still missing. In this study, we present a novel concept to close the loop in myoelectric prostheses. In addition to conveying the grasping force (system output), we provided to the user the online information about the system input (EMG biofeedback). Methods As a proof-of-concept, the EMG biofeedback was transmitted in the current study using a visual interface (ideal condition). Ten able-bodied subjects and two amputees controlled a state-of-the-art myoelectric prosthesis in routine grasping and force steering tasks using EMG and force feedback (novel approach) and force feedback only (classic approach). The outcome measures were the variability of the generated forces and absolute deviation from the target levels in the routine grasping task, and the root mean square tracking error and the number of sudden drops in the force steering task. Results During the routine grasping, the novel method when used by able-bodied subjects decreased twofold the force dispersion as well as absolute deviations from the target force levels, and also resulted in a more accurate and stable tracking of the reference force profiles during the force steering. Furthermore, the force variability during routine grasping did not increase for the higher target forces with EMG biofeedback. The trend was similar in the two amputees. Conclusions The study demonstrated that the subjects, including the two experienced users of a myoelectric prosthesis, were able to exploit the online EMG biofeedback to observe and modulate the myoelectric signals, generating thereby more consistent commands. This allowed them to control the force predictively (routine grasping) and with a finer resolution (force steering). The future step will be to implement this promising and simple approach using an electrotactile interface. A prosthesis with a reliable response, following faithfully user intentions, would improve the utility during daily-life use and also facilitate the embodiment of the assistive system."],["dc.identifier.doi","10.1186/s12984-015-0047-z"],["dc.identifier.pmid","26088323"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12335"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58507"],["dc.language.iso","en"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/286208/EU//MYOSENS"],["dc.relation.euproject","MYOSENS"],["dc.rights.access","openAccess"],["dc.rights.holder","Dosen et al."],["dc.title","EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","13465"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific reports"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Yavuz, Utku Ş."],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2019-07-09T11:44:37Z"],["dc.date.available","2019-07-09T11:44:37Z"],["dc.date.issued","2017"],["dc.description.abstract","Human motor function emerges from the interaction between the neuromuscular and the musculoskeletal systems. Despite the knowledge of the mechanisms underlying neural and mechanical functions, there is no relevant understanding of the neuro-mechanical interplay in the neuro-musculo-skeletal system. This currently represents the major challenge to the understanding of human movement. We address this challenge by proposing a paradigm for investigating spinal motor neuron contribution to skeletal joint mechanical function in the intact human in vivo. We employ multi-muscle spatial sampling and deconvolution of high-density fiber electrical activity to decode accurate α-motor neuron discharges across five lumbosacral segments in the human spinal cord. We use complete α-motor neuron discharge series to drive forward subject-specific models of the musculoskeletal system in open-loop with no corrective feedback. We perform validation tests where mechanical moments are estimated with no knowledge of reference data over unseen conditions. This enables accurate blinded estimation of ankle function purely from motor neuron information. Remarkably, this enables observing causal associations between spinal motor neuron activity and joint moment control. We provide a new class of neural data-driven musculoskeletal modeling formulations for bridging between movement neural and mechanical levels in vivo with implications for understanding motor physiology, pathology, and recovery."],["dc.identifier.doi","10.1038/s41598-017-13766-6"],["dc.identifier.pmid","29044165"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14841"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59050"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/737570/EU//INTERSPINE"],["dc.relation.issn","2045-2322"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.title","In Vivo Neuromechanics: Decoding Causal Motor Neuron Behavior with Resulting Musculoskeletal Function."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2547"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Experimental Brain Research"],["dc.bibliographiccitation.lastpage","2559"],["dc.bibliographiccitation.volume","235"],["dc.contributor.author","De Nunzio, Alessandro Marco"],["dc.contributor.author","Dosen, Strahinja"],["dc.contributor.author","Lemling, Sabrina"],["dc.contributor.author","Markovic, Marko"],["dc.contributor.author","Schweisfurth, Meike Annika"],["dc.contributor.author","Ge, Nan"],["dc.contributor.author","Graimann, Bernhard"],["dc.contributor.author","Falla, Deborah"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2019-07-09T11:44:27Z"],["dc.date.available","2019-07-09T11:44:27Z"],["dc.date.issued","2017"],["dc.description.abstract","Grasping is a complex task routinely performed in an anticipatory (feedforward) manner, where sensory feedback is responsible for learning and updating the internal model of grasp dynamics. This study aims at evaluating whether providing a proportional tactile force feedback during the myoelectric control of a prosthesis facilitates learning a stable internal model of the prosthesis force control. Ten able-bodied subjects controlled a sensorized myoelectric prosthesis performing four blocks of consecutive grasps at three levels of target force (30, 50, and 70%), repeatedly closing the fully opened hand. In the first and third block, the subjects received tactile and visual feedback, respectively, while during the second and fourth block, the feedback was removed. The subjects also performed an additional block with no feedback 1 day after the training (Retest). The median and interquartile range of the generated forces was computed to assess the accuracy and precision of force control. The results demonstrated that the feedback was indeed an effective instrument for the training of prosthesis control. After the training, the subjects were still able to accurately generate the desired force for the low and medium target (30 and 50% of maximum force available in a prosthesis), despite the feedback being removed within the session and during the retest (low target force). However, the training was substantially less successful for high forces (70% of prosthesis maximum force), where subjects exhibited a substantial loss of accuracy as soon as the feedback was removed. The precision of control decreased with higher forces and it was consistent across conditions, determined by an intrinsic variability of repeated myoelectric grasping. This study demonstrated that the subject could rely on the tactile feedback to adjust the motor command to the prosthesis across trials. The subjects adjusted the mean level of muscle activation (accuracy), whereas the precision could not be modulated as it depends on the intrinsic myoelectric variability. They were also able to maintain the feedforward command even after the feedback was removed, demonstrating thereby a stable learning, but the retention depended on the level of the target force. This is an important insight into the role of feedback as an instrument for learning of anticipatory prosthesis force control."],["dc.identifier.doi","10.1007/s00221-017-4991-7"],["dc.identifier.pmid","28550423"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14765"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59015"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Tactile feedback is an effective instrument for the training of grasping with a prosthesis at low- and medium-force levels"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","47"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of NeuroEngineering and Rehabilitation"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Kapelner, Tamás"],["dc.contributor.author","Vujaklija, Ivan"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Negro, Francesco"],["dc.contributor.author","Aszmann, Oskar C."],["dc.contributor.author","Principe, Jose"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2019-07-09T11:51:40Z"],["dc.date.available","2019-07-09T11:51:40Z"],["dc.date.issued","2019"],["dc.description.abstract","BACKGROUND: Current myoelectric control algorithms for active prostheses map time- and frequency-domain features of the interference EMG signal into prosthesis commands. With this approach, only a fraction of the available information content of the EMG is used and the resulting control fails to satisfy the majority of users. In this study, we predict joint angles of the three degrees of freedom of the wrist from motor unit discharge timings identified by decomposition of high-density surface EMG. METHODS: We recorded wrist kinematics and high-density surface EMG signals from six able-bodied individuals and one patient with limb deficiency while they performed movements of three degrees of freedom of the wrist at three different speeds. We compared the performance of linear regression to predict the observed individual wrist joint angles from, either traditional time domain features of the interference EMG or from motor unit discharge timings (which we termed neural features) obtained by EMG decomposition. In addition, we propose and test a simple model-based dimensionality reduction, based on the physiological notion that the discharge timings of motor units are partly correlated. RESULTS: The regression approach using neural features outperformed regression on classic global EMG features (average R2 for neural features 0.77 and 0.64, for able-bodied subjects and patients, respectively; for time-domain features 0.70 and 0.52). CONCLUSIONS: These results indicate that the use of neural information extracted from EMG decomposition can advance man-machine interfacing for prosthesis control."],["dc.identifier.doi","10.1186/s12984-019-0516-x"],["dc.identifier.pmid","30953528"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16168"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59987"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/267888/EU//DEMOVE"],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/737570/EU//INTERSPINE"],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/702491/EU//NeuralCon"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.title","Predicting wrist kinematics from motor unit discharge timings for the control of active prostheses"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","110"],["dc.bibliographiccitation.journal","Journal of NeuroEngineering and Rehabilitation"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Lorrain, Thomas"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:37:45Z"],["dc.date.available","2018-11-07T09:37:45Z"],["dc.date.issued","2014"],["dc.description.abstract","Background: Current clinical myoelectric systems provide unnatural prosthesis control, with limited functionality. In this study, we propose a proportional state-based control method, which allows switching between functions in a more natural and intuitive way than the traditional co-contraction switch method. Methods: We validated the ability of the proposed system to provide precise control in both position and velocity modes. Two tests were performed with online visual feedback, involving target reaching and direct force control in grasping. The performance of the system was evaluated both on a subject with limb deficiency and in 9 intact-limbed subjects, controlling two degrees of freedom (DoF) of the hand and wrist. Results: The system allowed completion of the tasks involving 1-DoF with task completion rate >96% and of those involving 2-DoF with completion rate >91%. When compared with the clinical/industrial state-of-the-art approach and with a classic pattern recognition approach, the proposed method significantly improved the performance in the 2-DoF tasks. The completion rate in grasping force control was >97% on average. Conclusions: These results indicate that, using the proposed system, subjects were successfully able to operate two DoFs, and to achieve precise force control in grasping. Thus, the proposed state-based method could be a suitable alternative for commercial myoelectric devices, providing reliable and intuitive control of two DoFs."],["dc.description.sponsorship","European Commission [251555, 280778]"],["dc.identifier.doi","10.1186/1743-0003-11-110"],["dc.identifier.isi","000339361000001"],["dc.identifier.pmid","25012766"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/32906"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1743-0003"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","A state-based, proportional myoelectric control method: online validation and comparison with the clinical state-of-the-art"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e92390"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Negro, Francesco"],["dc.contributor.author","Yavuz, Utku Suekrue"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:42:26Z"],["dc.date.available","2018-11-07T09:42:26Z"],["dc.date.issued","2014"],["dc.description.abstract","Contractile properties of human motor units provide information on the force capacity and fatigability of muscles. The spike-triggered averaging technique (STA) is a conventional method used to estimate the twitch waveform of single motor units in vivo by averaging the joint force signal. Several limitations of this technique have been previously discussed in an empirical way, using simulated and experimental data. In this study, we provide a theoretical analysis of this technique in the frequency domain and describe its intrinsic limitations. By analyzing the analytical expression of STA, first we show that a certain degree of correlation between the motor unit activities prevents an accurate estimation of the twitch force, even from relatively long recordings. Second, we show that the quality of the twitch estimates by STA is highly related to the relative variability of the inter-spike intervals of motor unit action potentials. Interestingly, if this variability is extremely high, correct estimates could be obtained even for high discharge rates. However, for physiological inter-spike interval variability and discharge rate, the technique performs with relatively low estimation accuracy and high estimation variance. Finally, we show that the selection of the triggers that are most distant from the previous and next, which is often suggested, is not an effective way for improving STA estimates and in some cases can even be detrimental. These results show the intrinsic limitations of the STA technique and provide a theoretical framework for the design of new methods for the measurement of motor unit force twitch."],["dc.description.sponsorship","European Research Council Advanced Grant DEMOVE [267888]"],["dc.identifier.doi","10.1371/journal.pone.0092390"],["dc.identifier.isi","000333675600035"],["dc.identifier.pmid","24667744"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10062"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33952"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Limitations of the Spike-Triggered Averaging for Estimating Motor Unit Twitch Force: A Theoretical Analysis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1333"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Systems and Rehabilitation Engineering"],["dc.bibliographiccitation.lastpage","1341"],["dc.bibliographiccitation.volume","24"],["dc.contributor.author","Hofmann, David"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Vujaklija, Ivan"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T10:05:01Z"],["dc.date.available","2018-11-07T10:05:01Z"],["dc.date.issued","2016"],["dc.description.abstract","The amplitude of the surface EMG (sEMG) is commonly estimated by rectification or other nonlinear transformations, followed by smoothing (low-pass linear filtering). Although computationally efficient, this approach leads to an estimation accuracy with a limited theoretical signal-to-noise ratio (SNR). Since sEMG amplitude is one of the most relevant features for myoelectric control, its estimate has become one of the limiting factors for the performance of myoelectric control applications, such as powered prostheses. In this study, we present a recursive nonlinear estimator of sEMG amplitude based on Bayesian filtering. Furthermore, we validate the advantage of the proposed Bayesian filter over the conventional linear filters through an online simultaneous and proportional control (SPC) task, performed by eight able-bodied subjects and three below-elbow limb deficient subjects. The results demonstrated that the proposed Bayesian filter provides significantly more accurate SPC, particularly for the patients, when compared with conventional linear filters. This result presents a major step toward accurate prosthetic control for advanced multi-function prostheses."],["dc.identifier.doi","10.1109/TNSRE.2015.2501979"],["dc.identifier.isi","000390559600007"],["dc.identifier.pmid","26600161"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14165"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/38814"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.issn","1558-0210"],["dc.relation.issn","1534-4320"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Bayesian Filtering of Surface EMG for Accurate Simultaneous and Proportional Prosthetic Control"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]