A Robust Sparse Representation Based Pattern Recognition Approach for Myoelectric Control

Interferences in the form of white Gaussian noise (WGN) are inevitable during long-term electromyogram (EMG) recordings. Even with the aid of advanced signal denoising techniques, such an intermittent interference is hardly detected and attenuated in the practical use of EMG-driven control systems. Hence, a robust pattern recognition scheme that is invariant to noise contamination would potentially aid the realization of an efficient EMG-based pattern recognition (EMG-PR) control system. To this end, an EMG-PR scheme driven by sparse representation-based classification (SRC) algorithm and root mean square (rms) descriptor (RMS-SRC) is proposed in this paper. The accuracy and the robustness of the proposed scheme were investigated using the high-density surface EMG recordings from 12 traumatic brain-injured patients and 5 post-stroke survivors. For benchmark comparison, another three different feature sets and four pattern recognition algorithms were considered. The optimal pattern recognition schemes with respect to each feature-classifier combination were first selected in the absence of WGN contamination. Then, six levels of WGN with a signal-to-noise ratio (SNR) ranging from 5 to 30 dB were added into the EMG recordings, respectively, to mimic the different WGN interferences. Our result showed that the proposed RMS-SRC scheme could achieve a similar accuracy with the benchmark schemes in the presence of limited noise contamination (0-15 dB), and significantly outperformed the other schemes when the SNR of WGN increased (20-30 dB). More notably, the RMS-SRC scheme significantly outperformed the other pattern recognition schemes when the WGN existed in either training set or testing set only. The findings proved the comparative advantage of the proposed RMS-SRC pattern recognition scheme over the other currently used schemes in the myoelectric control. Thus, the proposed scheme would potentially facilitate the development of EMG-driven rehabilitation robots for accurate and dexterous assistive training for patients with neurological disorders.