A Novel Feature Extraction for Robust EMG Pattern Recognition

By Angkoon Phinyomark, Chusak Limsakul, Pornchai Phukpattaranont. arXiv.org, 2009

423 citations recorded by the source corpus.

Varieties of noises are major problem in recognition of Electromyography (EMG) signal. Hence, methods to remove noise become most significant in EMG signal analysis. White Gaussian noise (WGN) is used to represent interference in this paper. Generally, WGN is difficult to be removed using typical filtering and solutions to remove WGN are limited. In addition, noise removal is an important step before performing feature extraction, which is used in EMG-based recognition. This research is aimed to present a novel feature that tolerate with WGN. As a result, noise removal algorithm is not needed. Two novel mean and median frequencies (MMNF and MMDF) are presented for robust feature extraction. Sixteen existing features and two novelties are evaluated in a noisy environment. WGN with various signal-to-noise ratios (SNRs), i.e. 20-0 dB, was added to the original EMG signal. The results showed that MMNF performed very well especially in weak EMG signal compared with others. The error of MMNF in weak EMG signal with very high noise, 0 dB SNR, is about 5-10% and closed by MMDF and Histogram, whereas the error of other features is more than 20%. While in strong EMG signal, the error of MMNF is better than those from other features. Moreover, the combination of MMNF, Histrogram of EMG and Willison amplitude is used as feature vector in classification task. The experimental result shows the better recognition result in noisy environment than other success feature candidates. From the above results demonstrate that MMNF can be used for new robust feature extraction. Index Terms—Electromyography (EMG), Feature extraction, Pattern recognition, Robustness, Man-machine interfaces. —————————— a ——————————

Subfields: Biomedical Engineering, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/3d857e8fa4bc59b614e6d220f2af644c3e886ba9

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Citations in the corpus

Mean and Median Frequency of EMG Signal to Determine Muscle Force based on Time- dependent Power Spectrum (2013, methodology): “Generally, features-based on time-domain and frequency-domain are two important feature ones [6].”
A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions (2016, methodology): “The conventional features had been modiﬁed by Phinyomark et al. in a way that robust features can be extracted to track the progression of fatigue over time [56].”
Feature reduction and selection for EMG signal classification (2012, background): “Modified mean absolute value type 2 (MAV2) is an expansion of MAV feature which is similar to the MAV1 (e.g. Oskoei & Hu, 2008; Phinyomark et al., 2009a).”
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals (2021, methodology): “And then we extract 91 features for EEG, EMG and ECG signals [68], [69], [70]; In Mental Fatigue dataset, we preprocess physiological signals by interpolation and filtering algorithm.”