Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors

By Angkoon Phinyomark, Rami N. Khushaba, Erik J. Scheme. Italian National Conference on Sensors, 2018

317 citations recorded by the source corpus.

Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent advancements in wearable sensors, wireless communication and embedded technologies, wearable electromyographic (EMG) armbands are now commercially available for the general public. Due to physical, processing, and cost constraints, however, these armbands typically sample EMG signals at a lower frequency (e.g., 200 Hz for the Myo armband) than their clinical counterparts. It remains unclear whether existing EMG feature extraction methods, which largely evolved based on EMG signals sampled at 1000 Hz or above, are still effective for use with these emerging lower-bandwidth systems. In this study, the effects of sampling rate (low: 200 Hz vs. high: 1000 Hz) on the classification of hand and finger movements were evaluated for twenty-six different individual features and eight sets of multiple features using a variety of datasets comprised of both able-bodied and amputee subjects. The results show that, on average, classification accuracies drop significantly (p< 0.05) from 2% to 56% depending on the evaluated features when using the lower sampling rate, and especially for transradial amputee subjects. Importantly, for these subjects, no number of existing features can be combined to compensate for this loss in higher-frequency content. From these results, we identify two new sets of recommended EMG features (along with a novel feature, L-scale) that provide better performance for these emerging low-sampling rate systems.

View Paper: https://www.semanticscholar.org/paper/0c78a685d311717c1415cbaefb5503955b941613

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Citations in the corpus

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning (2018, methodology): “The four feature sets employed for comparison purposes are: 1) Time Domain Features (TD) [37]: This set of features, which is probably the most commonly employed in the literature [29], often serves as the basis for bigger feature sets [1], [39], [34].”