On-line decomposition of iEMG signals using GPU-implemented Bayesian filtering

Tianyi Yu 1, 2, 3
2 SIMS - Signal, IMage et Son
LS2N - Laboratoire des Sciences du Numérique de Nantes
3 ReV - Robotique Et Vivant
LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : :A sequential decomposition algorithm based on a Hidden Markov Model of the EMG, that used Bayesian filtering to estimate the unknown parameters of discharge series of motor units was previously proposed in the laboratory LS2N. This algorithm has successfully decomposed the experimental iEMG signal with four motor units. However, the proposed algorithm demands a high time consuming. In this work, we firstly validated the proposed algorithm in a serial structure. We proposed some modifications for the activation process of the recruitment model in Hidden Markov Model and implemented two signal pre-processing techniques to improve the performance of the algorithm. Then, we realized a GPU-oriented implementation of this algorithm, as well as the modifications applied to the original model in order to achieve a real-time performance. We have achieved the decomposition of 10 experimental iEMG signals acquired from two different muscles, respectively by fine wire electrodes and needle electrodes. The number of motor units ranges from 2 to 8. The percentage of superposition, representing the complexity of iEMG signal, ranges from 6.56 % to 28.84 %. The accuracies of almost all experimental iEMG signals are more than90 %, except two signals at 30 % MVC (more than 85 %). Moreover, we realized the realtime decomposition for all these experimental signals by the parallel implementation. We are the first one that realizes the real time full decomposition of single channel iEMG signal with number of MUs up to 10, where full decomposition means resolving the superposition problem. For the signals with more than 10 MUs, we can also decompose them quickly, but not reaching the real time level.
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Tianyi Yu. On-line decomposition of iEMG signals using GPU-implemented Bayesian filtering. Signal and Image Processing. École centrale de Nantes, 2019. English. ⟨NNT : 2019ECDN0006⟩. ⟨tel-02407288⟩

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