Abstract
We study the changes of Motor Unit (MU) filters in MU identification from high-density surface electromyograms recorded during isokinetic dynamic contractions of biceps brachii muscle. We demonstrate that these changes can be predicted for limited changes of the joint angle by linearly extrapolating previously recorded changes, allowing for the linear prediction-correction paradigm of MU filter updating. We then demonstrate the efficiency of this paradigm by implementing MU filter updating by the Kalman filter and integrating it into the previously published Convolution Kernel Compensation (CKC) MU identification method. When compared to the original CKC method and the previously published cyclostationary CKC method devoted to MU identification in repeated dynamic contractions, the Kalman based MU filter prediction yielded a superior precision of MU firing tracking in dynamic contractions. In the case of relatively fast biceps brachii contractions with full elbow flexion and extension in 2s, the Kalman based MU filter prediction tracked 21.3 ± 1.8 MUs with an average sensitivity of 95.6 ± 7.0% and precision of 96.5 ± 3.5%. In the same conditions, the original CKC method identified 7.1 ± 2.0 MUs with an average sensitivity of 62.7 ± 20.1% and precision of 98.1 ± 3.9%, whereas cyclostationary CKC tracked 18.9 ± 2.0 MUs with an average sensitivity of 91.8 ± 12.2% and precision of 94.7 ± 5.1%.
Highlights
I DENTIFICATION of neural codes that govern human movement is gaining relevance in our aging society
We study the changes of Motor Unit (MU) filters in blind source separation of high-density surface electromyograms, recorded during dynamic contractions of biceps brachii muscle
The results of linear prediction of Motor Unit Action Potential (MUAP) changes are reported in Subsection III-A, whereas the Subsection III-B reports the results of MU tracking by the Kalman filter
Summary
I DENTIFICATION of neural codes that govern human movement is gaining relevance in our aging society. The rapid development of systems for acquisition and processing of multichannel intramuscular [4] [5] or surface electromyographic (EMG) recordings [6] enabled the identification of the codes of several tens of simultaneously active motor nerves [7] [8] [9]. In this measuring setup, the skeletal muscles act as natural amplifiers of neural codes. MUAPs are strong enough to support their detection on the surface of the skin, above the investigated muscle [12]
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