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

Abstract

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.

EMG Pattern Recognition in the Era of Big Data and Deep Learning

“…Either due to instrumentation limitations, or to limit the volume of data, unfortunately some of these datasets sampled EMG signals at lower frequencies, such as 15.5 Hz for the Healey and Picard dataset and 512 Hz for DEAP and BioVid Emo DB datasets (see Table 4). These lie well below the typical 1000-Hz sampling frequency for EMG signals, below which the performance of EMG pattern recognition has been shown to suffer from the loss of high frequency information [26,30].…”

“…This is a critical aspect of both big data and EMG research, since sub-populations and different experimental conditions routinely favor different features and algorithms that are not shared by others. Therefore, no single EMG data set, big or not, should be considered to be comprehensive, and cross-validation of multiple datasets is recommended for the development of robust EMG pattern recognition systems [22,26]. Although larger EMG data sets would be preferable, the current publicly available EMG datasets (Tables 1-3) are sufficient to shed some light on the generalizability and robustness of EMG pattern recognition (and, in particular, myoelectric control).…”

“…Because theperformance of EMG pattern recognition can vary depending on subject, experimental protocol, acquisition setup, and differences in pre-processing, multiple datasets are needed to ensure the robustness and generalization of findings [22,26].…”

“…Khushaba et al [29] proposed a novel set of time domain features that can estimate the EMG signal power spectrum characteristics using five different EMG data sets. Phinyomark et al [26,30] investigated the effect of sampling rate on EMG pattern recognition and then identified a novel set of features that are more accurate and robust for emerging low-sampling rate EMG systems, using four different EMG data sets containing 40 subject sessions with over 8000 separate contractions.…”

Development of an EMG-Controlled Mobile Robot

“…Several studies have explored potential feature selection approaches for raw EMG signals [21,22]. The features chosen for this work were Integrated EMG (IEMG), Mean Average Value (MAV), Simple Square Integral (SSI), Root Mean Square (RMS), Log Detector (LOG) and Variance (VAR) [21,[23][24][25], primarily because these features are easy to compute and are computationally less intensive:…”