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Tamás Kapelner
University of Göttingen
ElectromyographyProsthesisReinnervationMotor unitPhysical medicine and rehabilitation
5Publications
4H-index
90Citations
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Publications 6
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#1Tamás Kapelner (GAU: University of Göttingen)H-Index: 4
#2Massimo Sartori (UT: University of Twente)H-Index: 17
Last. Dario Farina (Imperial College London)H-Index: 76
view all 4 authors...
We propose a myoelectric control method based on neural data regression and musculoskeletal modeling. This paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-EMG) decomposition to estimate muscle excitations. The muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on...
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#1Tamás Kapelner (GAU: University of Göttingen)H-Index: 4
#2Ivan Vujaklija (Aalto University)H-Index: 11
Last. Dario Farina (Imperial College London)H-Index: 76
view all 7 authors...
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...
1 CitationsSource
#1Tamás Kapelner (GAU: University of Göttingen)H-Index: 4
#2Francesco Negro (University of Brescia)H-Index: 46
Last. Dario Farina (Imperial College London)H-Index: 76
view all 4 authors...
We prove the feasibility of decomposing high density surface EMG signals from forearm muscles in non-isometric wrist motor tasks of normally limbed and limb-deficient individuals with the perspective of using the decoded neural information for prosthesis control. For this purpose, we recorded surface EMG signals during motions of three degrees of freedom of the wrist in seven normally limbed subjects and two patients with limb deficiency. The signals were decomposed into individual motor unit ac...
15 CitationsSource
#1Dario Farina (Imperial College London)H-Index: 76
#2Ivan Vujaklija (Imperial College London)H-Index: 11
Last. Oskar C. Aszmann (Medical University of Vienna)H-Index: 25
view all 10 authors...
The intuitive control of upper-limb prostheses requires a man/machine interface that directly exploits biological signals. Here, we define and experimentally test an offline man/machine interface that takes advantage of the discharge timings of spinal motor neurons. The motor-neuron behaviour is identified by deconvolution of the electrical activity of muscles reinnervated by nerves of a missing limb in patients with amputation at the shoulder or humeral level. We mapped the series of motor-neur...
54 CitationsSource
#1Tamás KapelnerH-Index: 4
#2Ning JiangH-Index: 29
Last. Oskar C. AszmannH-Index: 25
view all 7 authors...
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 cont...
14 CitationsSource
Apr 1, 2015 in NER (International IEEE/EMBS Conference on Neural Engineering)
#1Tamás Kapelner (GAU: University of Göttingen)H-Index: 4
#2Ning Jiang (GAU: University of Göttingen)H-Index: 29
Last. Dario Farina (GAU: University of Göttingen)H-Index: 76
view all 6 authors...
For the past six decades, signal processing methods for myoelectric control of prostheses consisted mainly of calculating time- and frequency domain features of the EMG signal. This type of feature extraction considers the surface EMG as colored noise, neglecting its generation as a sum of motor unit activities. In this study we propose the use of motor unit behavior for classifying motor tasks with the aim of myoelectric control. We recorded high-density surface EMG of three patients who underw...
6 CitationsSource
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