Motor Unit Identification From High-Density Surface Electromyograms in Repeated Dynamic Muscle Contractions

Abstract

We describe the method for identification of motor unit (MU) firings from high-density surface electromyograms (hdEMG), recorded during repeated dynamic muscle contractions. A new convolutive data model for dynamic hdEMG is presented, along with the pulse-tonoise ratio (PNR) metric for assessment of MU identification accuracy and analysis of the impact of MU action potential (MUAP) changes in dynamic muscle contractions on MU identification. We tested the presented methodology on signals from biceps brachii, vastus lateralis, and rectus famoris muscles, all during different speeds of dynamic contractions. In synthetic signals with excitation levels of 10%, 30% and 50%, and MUAPs experimentally recorded from biceps brachii muscle, the presented method identified 15 ± 1, 18 ± 1, and 20 ± 1 MUs per contraction, respectively, all with average sensitivity and precision >90% and PNR >30 dB. In experimental signals acquired during low force contractions of vastus lateralis and rectus femoris muscle, the method identified 9.4 ± 1.9 and 7.8 ± 1.4 MUs with PNR values of 35.4±3.6 and 34.1±2.7 dB. In comparison with the previously introduced Convolution Kernel Compensation method, the capability of the new method to follow dynamic MUAP changes is confirmed, also in relatively fast muscle contractions.

Robotic Wrist Rehabilitation With High-density Electromyographic Biofeedback for Reducing Co-activation of Antagonist Muscles and Improved Motor Coordination: A Feasibility Study

“…For example the detailed analysis of raw HDEMG also shows that in spite of the signi cantly higher RMS in the 3 rd week we have not noticed a clear co-activation similar to the one of the antagonists at wrist extension but rather a constant muscle activity. For a successful selection of desired motor units [9] we may further examine the mesh of RMS HDEMG values.…”

“…Recent advancement in technologies and signal processing enabled automatic detection of low quality signals, interpolation and generating a high density EMG map [7]. A high-density surface EMG has been used as an e cient diagnostic tools [8], particularly in the development of novel motor unit detection algorithms [9]. The high-density EMG (HDEMG) contains a large amount of information and to the best of our knowledge it has not been used as a biofeedback yet, but rather as an input to the myoelectric prosthetic hand control [10].…”

Torque Estimation Using Neural Drive for a Concentric Contraction

“…For a constant force nonisometric contraction, a majority of the non-stationarity will be produced by the lengthening and shortening of muscle fibers. Thus, similar to the method proposed in Glaser and Holobar [9], we propose that decomposing short segments of the HDsEMG during a slow-moving, isotonic contraction will result in identifying a pool of motor units that better represent the joint output force than traditional sEMG torque estimation approaches.…”

“…An important assumption of these algorithms is signal stationarity, which is violated during nonisometric muscle contractions. Efforts have been made to design a non-isometric test condition in which the stationarity assumption is less violated [8], [9]. One proposed condition is a very slow contraction in which the force is constant (an isotonic contraction) [9].…”

“…Efforts have been made to design a non-isometric test condition in which the stationarity assumption is less violated [8], [9]. One proposed condition is a very slow contraction in which the force is constant (an isotonic contraction) [9]. Force variation is also a source of non-stationarity; the frequency content of the sEMG will change as a function of time [10].…”